Systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking

ABSTRACT

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 17/214,593, filed Mar. 26, 2021, and titled SYSTEMS, METHODS,AND DEVICES FOR MEDICAL IMAGE ANALYSIS, DIAGNOSIS, RISK STRATIFICATION,DECISION MAKING AND/OR DISEASE TRACKING, which is a continuation of U.S.patent application Ser. No. 17/142,120, filed Jan. 5, 2021, and titledSYSTEMS, METHODS, AND DEVICES FOR MEDICAL IMAGE ANALYSIS, DIAGNOSIS,RISK STRATIFICATION, DECISION MAKING AND/OR DISEASE TRACKING, whichclaims the benefit of U.S. Provisional Patent Application No.62/958,032, filed Jan. 7, 2020, and titled SYSTEMS, METHODS, AND DEVICESFOR CARDIOVASCULAR IMAGE ANALYSIS, DIAGNOSIS, RISK STRATIFICATION,DECISION MAKING AND/OR DISEASE TRACKING, each of which is incorporatedherein by reference in its entirety under 37 C.F.R. § 1.57. Any and allapplications for which a foreign or domestic priority claim isidentified in the Application Data Sheet as filed with the presentapplication are hereby incorporated by reference under 37 C.F.R. § 1.57.

BACKGROUND Field

The present application relates to systems, methods, and devices formedical image analysis, diagnosis, risk stratification, decision makingand/or disease tracking.

Description

Coronary heart disease affects over 17.6 million Americans. The currenttrend in treating cardiovascular health issues is generally two-fold.First, physicians generally review a patient's cardiovascular healthfrom a macro level, for example, by analyzing the biochemistry or bloodcontent or biomarkers of a patient to determine whether there are highlevels of cholesterol elements in the bloodstream of a patient. Inresponse to high levels of cholesterol, some physicians will prescribeone or more drugs, such as statins, as part of a treatment plan in orderto decrease what is perceived as high levels of cholesterol elements inthe bloodstream of the patient.

The second general trend for currently treating cardiovascular healthissues involves physicians evaluating a patient's cardiovascular healththrough the use of angiography to identify large blockages in variousarteries of a patient. In response to finding large blockages in variousarteries, physicians in some cases will perform an angioplasty procedurewherein a balloon catheter is guided to the point of narrowing in thevessel. After properly positioned, the balloon is inflated to compressor flatten the plaque or fatty matter into the artery wall and/or tostretch the artery open to increase the flow of blood through the vesseland/or to the heart. In some cases, the balloon is used to position andexpand a stent within the vessel to compress the plaque and/or maintainthe opening of the vessel to allow more blood to flow. About 500,000heart stent procedures are performed each year in the United States.

However, a recent federally funded $100 million study calls intoquestion whether the current trends in treating cardiovascular diseaseare the most effective treatment for all types of patients. The recentstudy involved over 5,000 patients with moderate to severe stable heartdisease from 320 sites in 37 countries and provided new evidence showingthat stents and bypass surgical procedures are likely no more effectivethan drugs combined with lifestyle changes for people with stable heartdisease. Accordingly, it may be more advantageous for patients withstable heart disease to forgo invasive surgical procedures, such asangioplasty and/or heart bypass, and instead be prescribed heartmedicines, such as statins, and certain lifestyle changes, such asregular exercise. This new treatment regimen could affect thousands ofpatients worldwide. Of the estimated 500,000 heart stent proceduresperformed annually in the United States, it is estimated that a fifth ofthose are for people with stable heart disease. It is further estimatedthat 25% of the estimated 100,000 people with stable heart disease, orroughly 23,000 people, are individuals that do not experience any chestpain. Accordingly, over 20,000 patients annually could potentially forgoinvasive surgical procedures or the complications resulting from suchprocedures.

To determine whether a patient should forego invasive surgicalprocedures and opt instead for a drug regimen, it can be important tomore fully understand the cardiovascular disease of a patient.Specifically, it can be advantageous to better understand the arterialvessel health of a patient.

SUMMARY

Various embodiments described herein relate to systems, methods, anddevices for medical image analysis, diagnosis, risk stratification,decision making and/or disease tracking.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein are configured to utilize non-invasive medical imagingtechnologies, such as a CT image for example, which can be inputted intoa computer system configured to automatically and/or dynamically analyzethe medical image to identify one or more coronary arteries and/orplaque within the same. For example, in some embodiments, the system canbe configured to utilize one or more machine learning and/or artificialintelligence algorithms to automatically and/or dynamically analyze amedical image to identify, quantify, and/or classify one or morecoronary arteries and/or plaque. In some embodiments, the system can befurther configured to utilize the identified, quantified, and/orclassified one or more coronary arteries and/or plaque to generate atreatment plan, track disease progression, and/or a patient-specificmedical report, for example using one or more artificial intelligenceand/or machine learning algorithms. In some embodiments, the system canbe further configured to dynamically and/or automatically generate avisualization of the identified, quantified, and/or classified one ormore coronary arteries and/or plaque, for example in the form of agraphical user interface. Further, in some embodiments, to calibratemedical images obtained from different medical imaging scanners and/ordifferent scan parameters or environments, the system can be configuredto utilize a normalization device comprising one or more compartments ofone or more materials.

In some embodiments, a normalization device configured to facilitatenormalization of medical images of a coronary region of a subject for analgorithm-based medical imaging analysis is provided, wherein thenormalization device comprises: a substrate having a width, a length,and a depth dimension, the substrate having a proximal surface and adistal surface, the proximal surface adapted to be placed adjacent to asurface of a body portion of the subject; a plurality of compartmentspositioned within the substrate, each of the plurality of compartmentsconfigured to hold a sample of a known material, wherein: a first subsetof the plurality of compartments hold at least one sample of a contrastmaterial, a second subset of the plurality of compartments hold samplesof materials representative of materials to be analyzed by thealgorithm-based medical imaging analysis, wherein the samples ofmaterials representative of materials comprise at least two of calcium1000 HU, calcium 220 HU, calcium 150 HU, calcium 130 HU, and a lowattenuation material of 30 HU, and a third subset of the plurality ofcompartments hold at least one sample of phantom material; and anadhesive on the proximal surface of the substrate and configured toadhere the normalization device to the body portion patient.

In some embodiments of the normalization device, wherein samples ofmaterials representative of materials to be analyzed comprise calcium1000 HU, calcium 220 HU, calcium 150 HU, calcium 130 HU, and a lowattenuation material of 30 HU. In some embodiments of the normalizationdevice, the at least one contrast material comprises one or more ofiodine, Gad, Tantalum, Tungsten, Gold, Bismuth, or Ytterbium; and the atleast one sample of phantom material comprise one or more of water, fat,calcium, uric acid, air, iron, or blood.

In some embodiments of the normalization device, the substratecomprises: a first layer, and at least some of the plurality ofcompartments are positioned in the first layer in a first arrangement;and a second layer positioned above the first layer, and at least someof the plurality of compartments are positioned in the second layerincluding in a second arrangement. In some embodiments of thenormalization device, at least one of the compartments is configured tobe self-sealing such that the sample can be injected into theself-sealing compartment and the compartment seals to contain theinjected material.

In some embodiments, a computer-implemented method for normalizingmedical images for an algorithm-based medical imaging analysis using anormalization device is provided, wherein normalization of the medicalimages improves accuracy of the algorithm-based medical imaginganalysis, the method comprising: accessing, by a computer system, afirst medical image of a coronary region of a subject and thenormalization device, wherein the first medical image is obtainednon-invasively; accessing, by the computer system, a second medicalimage of a coronary region of a subject and the normalization device,wherein the second medical image is obtained non-invasively, and whereinthe first medical image and the second medical image comprise at leastone of the following: one or more first variable acquisition parametersassociated with capture of the first medical image differ from acorresponding one or more second variable acquisition parametersassociated with capture of the second medical image, a first imagecapture technology used to capture the first medical image differs froma second image capture technology used to capture the second medicalimage, or a first contrast agent used during the capture of the firstmedical image differs from a second contrast agent used during thecapture of the second medical image; identifying, by the computersystem, first image parameters of the normalization device within thefirst medical image; generating a normalized first medical image for thealgorithm-based medical imaging analysis based in part on the firstidentified image parameters of the normalization device within the firstmedical image; identifying, by the computer system, second imageparameters of the normalization device within the second medical image;and generating a normalized second medical image for the algorithm-basedmedical imaging analysis based in part on the second identified imageparameters of the normalization device within the second medical image,wherein the computer system comprises a computer processor and anelectronic storage medium. In some embodiments of a computer-implementedmethod for normalizing medical images for an algorithm-based medicalimaging analysis using a normalization device, the algorithm-basedmedical imaging analysis comprises an artificial intelligence or machinelearning imaging analysis algorithm, wherein the artificial intelligenceor machine learning imaging analysis algorithm was trained using imagesthat included the normalization device.

In some embodiments, a computer-implemented method of quantifying andclassifying coronary plaque within a coronary region of a subject basedon non-invasive medical image analysis is provided, the methodcomprising: accessing, by a computer system, a medical image of acoronary region of a subject, wherein the medical image of the coronaryregion of the subject is obtained non-invasively; identifying, by thecomputer system utilizing a coronary artery identification algorithm,one or more coronary arteries within the medical image of the coronaryregion of the subject, wherein the coronary artery identificationalgorithm is configured to utilize raw medical images as input;identifying, by the computer system utilizing a plaque identificationalgorithm, one or more regions of plaque within the one or more coronaryarteries identified from the medical image of the coronary region of thesubject, wherein the plaque identification algorithm is configured toutilize raw medical images as input; determining, by the computersystem, one or more vascular morphology parameters and a set ofquantified plaque parameters of the one or more identified regions ofplaque from the medical image of the coronary region of the subject,wherein the set of quantified plaque parameters comprises a ratio orfunction of volume to surface area, heterogeneity index, geometry, andradiodensity of the one or more regions of plaque within the medicalimage; generating, by the computer system, a weighted measure of thedetermined one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaque; andclassifying, by the computer system, the one or more regions of plaquewithin the medical image as stable plaque or unstable plaque based atleast in part on the generated weighted measure of the determined one ormore vascular morphology parameters and the determined set of quantifiedplaque parameters, wherein the computer system comprises a computerprocessor and an electronic storage medium.

In some embodiments of a computer-implemented method of quantifying andclassifying coronary plaque within a coronary region of a subject basedon non-invasive medical image analysis, a ratio of volume to surfacearea of the one or more regions of plaque below a predeterminedthreshold is indicative of stable plaque. In some embodiments of acomputer-implemented method of quantifying and classifying coronaryplaque within a coronary region of a subject based on non-invasivemedical image analysis, a heterogeneity of the one or more regions ofplaque below a predetermined threshold is indicative of stable plaque.In some embodiments of a computer-implemented method of quantifying andclassifying coronary plaque within a coronary region of a subject basedon non-invasive medical image analysis, the heterogeneity index of oneor more regions of plaque is determined by generating spatial mapping ofradiodensity values across the one or more regions of plaque.

In some embodiments of a computer-implemented method of quantifying andclassifying coronary plaque within a coronary region of a subject basedon non-invasive medical image analysis, the method further comprisesgenerating, by the computer system, an assessment of the subject for oneor more of atherosclerosis, stenosis, or ischemia based at least in parton the classified one or more regions of plaque. In some embodiments ofa computer-implemented method of quantifying and classifying coronaryplaque within a coronary region of a subject based on non-invasivemedical image analysis, the medical image is obtained using an imagingtechnique comprising one or more of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS). In some embodiments of acomputer-implemented method of quantifying and classifying coronaryplaque within a coronary region of a subject based on non-invasivemedical image analysis, the one or more vascular morphology parameterscomprises a classification of arterial remodeling.

In some embodiments, a method for analyzing CT images and correspondinginformation is provided, the method comprising: storingcomputer-executable instructions, a set of computed tomography (CT)images of a patient's coronary vessels, vessel labels, and arteryinformation associated with the set of CT images including informationindicative of stenosis and plaque of segments of the coronary vessels,and indicative of locations of the coronary vessels; generating anddisplaying in a user interface a first panel including an artery treecomprising a three-dimensional (3D) representation of coronary vesselsbased on the CT images and depicting coronary vessels identified in theCT images, and depicting segment labels, the artery tree not includingheart tissue between branches of the artery tree; receiving a firstinput indicating a selection of a coronary vessel in the artery tree inthe first panel; in response to the first input, generating anddisplaying on the user interface a second panel illustrating at least aportion of the selected coronary vessel in at least one straightenedmultiplanar vessel (SMPR) view; generating and displaying on the userinterface a third panel showing a cross-sectional view of the selectedcoronary vessel, the cross-sectional view generated using one of the setof CT images of the selected coronary vessel, wherein locations alongthe at least one SMPR view are each associated with one of the CT imagesin the set of CT images such that a selection of a particular locationalong the coronary vessel in the at least one SMPR view displays theassociated CT image in the cross-sectional view in the third panel;generating and displaying on the user interface a fourth panel showingat least one anatomical plane view of the selected coronary vessel basedon the set of stored CT images, wherein the method is performed by oneor more computer hardware processors executing computer-executableinstructions stored on one or more non-transitory computer storagemediums.

In some embodiments of a method for analyzing CT images andcorresponding information, one or more anatomical plane views include anaxial plane view, a coronal plane view, and a sagittal plane view eachcorresponding to the selected coronary vessel. In some embodiments of amethod for analyzing CT images and corresponding information, the methodfurther comprises receiving a second input on the second panel of theuser interface indicating a first location along the selected coronaryvessel in the at least one SMPR view, and in response to the secondinput, generating and displaying in the cross-sectional view in thethird panel a CT image associated with the first location of theselected coronary vessel, and generating and displaying in the fourthpanel an axial plane view, a coronal plane view, and a sagittal planeview of the selected coronary vessel that correspond to the selectedcoronary vessel at the first location. In some embodiments of a methodfor analyzing CT images and corresponding information, the methodfurther comprises receiving a third input on the second panel pf theuser interface indicating a second location along the selected coronaryvessel in the at least one SMPR view, and in response to the thirdinput, generating and displaying in the cross-sectional view in thethird panel a CT image associated with the second location of theselected coronary vessel, and generating and displaying in the fourthpanel an axial plane view, a coronal plane view, and a sagittal planeview of the selected coronary vessel that correspond to the selectedcoronary vessel at the second location.

In some embodiments of a method for analyzing CT images andcorresponding information, the method further comprises generating anddisplaying segment name labels, proximal to a respective segment on theartery tree, indicative of the name of the segment, using the arteryinformation, and in response to an input selection of a first segmentname label displayed on the user interface, generating and displaying onthe user interface a panel having a list of vessel segment names andindicating the current name of the selected vessel segment, and inresponse to an input selection of a second segment name label on thelist, replacing the first segment name label with the second segmentname label of the displayed artery tree in the user interface. In someembodiments of a method for analyzing CT images and correspondinginformation, the method further comprises generating and displaying onthe user interface in a cartoon artery tree, the cartoon artery treecomprising a non-patient specific graphical representation of a coronaryartery tree, and wherein in response to a selection of a vessel segmentin the cartoon artery tree, a view of the selected vessel segment isdisplayed in the user interface in a SMPR view, and upon selection of alocation of the vessel segment displayed in the SMPR view, generatingand displaying in the user interface a panel that displays informationrelated to stenosis or plaque of the selected vessel segment at theselected location. In some embodiments of a method for analyzing CTimages and corresponding information, the method further comprisesgenerating and displaying a tool bar on a the user interface, the toolbar comprising at least one of the following tools: a lumen wall tool, asnap to vessel wall tool, a snap to lumen wall tool, vessel wall tool, asegment tool, a stenosis tool, a plaque overlay tool a snap tocenterline tool, chronic total occlusion tool, stent tool, an excludetool, a tracker tool, or a distance measurement tool.

For purposes of this summary, certain aspects, advantages, and novelfeatures of the invention are described herein. It is to be understoodthat not necessarily all such advantages may be achieved in accordancewith any particular embodiment of the invention. Thus, for example,those skilled in the art will recognize that the invention may beembodied or carried out in a manner that achieves one advantage or groupof advantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

All of these embodiments are intended to be within the scope of theinvention herein disclosed. These and other embodiments will becomereadily apparent to those skilled in the art from the following detaileddescription having reference to the attached figures, the invention notbeing limited to any particular disclosed embodiment(s).

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in conjunction withthe accompanying drawings, which are incorporated in and constitute apart of this specification, and are provided to illustrate and provide afurther understanding of example embodiments, and not to limit thedisclosed aspects. In the drawings, like designations denote likeelements unless otherwise stated.

FIG. 1 is a flowchart illustrating an overview of an exampleembodiment(s) of a method for medical image analysis, visualization,risk assessment, disease tracking, treatment generation, and/or patientreport generation.

FIG. 2A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for analysis and classification of plaque froma medical image.

FIG. 2B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of non-calcified plaque froma non-contrast CT image(s).

FIG. 3A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for risk assessment based on medical imageanalysis.

FIG. 3B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for quantification of atherosclerosis based onmedical image analysis.

FIG. 3C is a flowchart illustrating an overview of an exampleembodiment(s) of a method for quantification of stenosis and generationof a CAD-RADS score based on medical image analysis.

FIG. 3D is a flowchart illustrating an overview of an exampleembodiment(s) of a method for disease tracking based on medical imageanalysis.

FIG. 3E is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of cause of change incalcium score based on medical image analysis.

FIG. 4A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for prognosis of a cardiovascular event basedon medical image analysis.

FIG. 4B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of patient-specific stentparameters based on medical image analysis.

FIG. 5A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for generation of a patient-specific medicalreport based on medical image analysis.

FIGS. 5B-5I illustrate example embodiment(s) of a patient-specificmedical report generated based on medical image analysis.

FIG. 6A illustrates an example of a user interface that can be generatedand displayed on the system, the user interface having multiple panels(views) that can show various corresponding views of a patient'sarteries.

FIG. 6B illustrates an example of a user interface that can be generatedand displayed on the system, the user interface having multiple panelsthat can show various corresponding views of a patient's arteries.

FIGS. 6C, 6D, and 6E illustrate certain details of a multiplanarreformat (MPR) vessel view in the second panel, and certainfunctionality associated with this view.

FIG. 6F illustrates an example of a three-dimensional (3D) rendering ofa coronary artery tree that allows a user to view the vessels and modifythe labels of a vessel.

FIG. 6G illustrates an example of a panel of the user interface thatprovides shortcut commands that a user may employ while analyzinginformation in the user interface in a coronary artery tree view, anaxial view, a sagittal view, and a coronal view.

FIG. 6H illustrates examples of panels of the user interface for viewingDICOM images in three anatomical planes: axial, coronal, and sagittal.

FIG. 6I illustrates an example of a panel of the user interface showinga cross-sectional view of a vessel, in the graphical overlay of anextracted feature of the vessel.

FIG. 6J illustrates an example of a toolbar that allows a user to selectdifferent vessels for review and analysis.

FIG. 6K illustrates an example of a series selection panel of the userinterface in an expanded view of the toolbar illustrated in FIG. 6J,which allows a user to expand the menu to view all the series (set ofimages) that are available for review and analysis for a particularpatient.

FIG. 6L illustrates an example of a selection panel that can bedisplayed on the user interface that may be uses to select a vesselsegment for analysis.

FIG. 6M illustrates an example of a panel that can be displayed on theuser interface to add a new vessel on the image.

FIG. 6N illustrates examples of two panels that can be displayed on theuser interface to name, or to rename, a vessel in the 3-D artery treeview.

FIG. 7A illustrates an example of an editing toolbar which allows usersto modify and improve the accuracy of the findings resulting fromprocessing CT scans with a machine learning algorithm and then by ananalyst.

FIGS. 7B and 7C illustrate examples of certain functionality of thetracker tool.

FIGS. 7D and 7E illustrate certain functionality of the vessel and lumenwall tools, which are used to modify the lumen and vessel wall contours.

FIG. 7F illustrates the lumen snap tool button (left) in the vessel snaptool button (right) on a user interface which can be used to activatethese tools.

FIG. 7G illustrates an example of a panel that can be displayed on theuser interface while using the lumen snap tool in the vessel snap tool.

FIG. 7H illustrates an example of a panel of the user interface that canbe displayed while using the segment tool which allows for marking theboundaries between individual coronary segments on the MPR.

FIG. 7I illustrates an example of a panel of the user interface thatallows a different name to be selected for a segment.

FIG. 7J illustrates an example of a panel of the user interface that canbe displayed while using the stenosis tool, which allows a user toindicate markers to mark areas of stenosis on a vessel.

FIG. 7K illustrates an example of a stenosis button of the userinterface which can be used to drop five evenly spaced stenosis markers.

FIG. 7L illustrates an example of a stenosis button of the userinterface which can be used to drop stenosis markers based on the useredited lumen and vessel wall contours.

FIG. 7M illustrates the stenosis markers on segments on a curvedmultiplanar vessel (CMPR) view.

FIG. 7N illustrates an example of a panel of the user interface that canbe displayed while using the plaque overlay tool.

FIGS. 7O and 7P illustrate a button on the user interface that can beselected to the plaque thresholds.

FIG. 7Q illustrates a panel of the user interface which can receive auser input to adjust plaque threshold levels for low-density plaque,non-calcified plaque, and calcified plaque.

FIG. 7R illustrates a cross-sectional view of a vessel indicating areasof plaque which are displayed in the user interface in accordance withthe plaque thresholds.

FIG. 7S illustrates a panel can be displayed showing plaque thresholdsin a vessel statistics panel that includes information on the vesselbeing viewed.

FIG. 7T illustrates a panel showing a cross-sectional view of a vesselthat can be displayed while using the centerline tool, which allowsadjustment of the center of the lumen.

FIGS. 7U, 7V, 7W illustrate examples of panels showing other views of avessel that can be displayed when using the centerline tool. FIG. 7U isan example of a view that can be displayed when extending the centerlineof a vessel. FIG. 7V illustrates an example of a view that can bedisplayed when saving or canceling centerline edits. FIG. 7W is anexample of a CMPR view that can be displayed when editing the vesselcenterline.

FIG. 7X illustrates an example of a panel that can be displayed whileusing the chronic total occlusion (CTO) tool, which is used to indicatea portion of artery with 100% stenosis and no detectable blood flow.

FIG. 7Y illustrates an example of a panel that can be displayed whileusing the stent tool, which allows a user to mark the extent of a stentin a vessel.

FIGS. 7Z and 7AA illustrates examples of panels that can be displayedwhile using the exclude tool, which allows a portion of the vessel to beexcluded from the analysis, for example, due to image aberrations. A row

FIGS. 7AB and 7AC illustrate examples of additional panels that can bedisplayed while using the exclude tool. FIG. 7AB illustrates a panelthat can be used to add a new exclusion. FIG. 7AC illustrates a panelthat can be used to add a reason for the exclusion.

FIGS. 7AD, 7AE, 7AF, and 7AG illustrate examples of panels that can bedisplayed while using the distance tool, which can be used to measurethe distance between two points on an image. For example, FIG. 7ADillustrates the distance tool being used to measure a distance on anSMPR view. FIG. 7AE illustrates the distance tool being used to measurea distance on an CMPR view. FIG. 7AF illustrates the distance will beused to measure a distance on a cross-sectional view of the vessel. FIG.7AG illustrates the distance tool being used to measure a distance on anaxial view.

FIG. 7AH illustrates a “vessel statistics” portion (button) of a panelwhich can be selected to display the vessel statistics tab.

FIG. 7AI illustrates the vessel statistics tab.

FIG. 7AJ illustrates functionality on the vessel statistics tab thatallows a user to click through the details of multiple lesions.

FIG. 7AK further illustrates an example of the vessel panel which theuser can use to toggle between vessels.

FIG. 8A illustrates an example of a panel of the user interface thatshows stenosis, atherosclerosis, and CAD-RADS results of the analysis.

FIG. 8B illustrates an example of a portion of a panel displayed on theuser interface that allows selection of a territory or combination ofterritories (e.g., left main artery (LM), left anterior descendingartery (LAD), left circumflex artery (LCx), right coronary artery (RCA),according to various embodiments.

FIG. 8C illustrates an example of a panel that can be displayed on theuser interface showing a cartoon representation of a coronary arterytree (“cartoon artery tree”).

FIG. 8D illustrates an example of a panel that can be displayed on theuser interface illustrating territory selection using the cartoon arterytree.

FIG. 8E illustrates an example panel that can be displayed on the userinterface showing per-territory summaries.

FIG. 8F illustrates an example panel that can be displayed on the userinterface showing a SMPR view of a selected vessel, and correspondingstatistics of the selected vessel.

FIG. 8G illustrates an example of a portion of a panel that can bedisplayed in the user interface indicating the presence of a stent,which is displayed at the segment level.

FIG. 8H illustrates an example of a portion of a panel that can bedisplayed in the user interface indicating CTO presence at the segmentlevel.

FIG. 8I illustrates an example of a portion of a panel that can bedisplayed in the user interface indicating left or right dominance ofthe patient.

FIG. 8J illustrates an example of a panel that can be displayed on theuser interface showing cartoon artery tree with indications of anomaliesthat were found.

FIG. 8K illustrates an example of a portion of a panel that can bedisplayed on the panel of FIG. 8J that can be selected to show detailsof an anomaly.

FIG. 9A illustrates an example of an atherosclerosis panel that can bedisplayed on the user interface which displays a summary ofatherosclerosis information based on the analysis.

FIG. 9B illustrates an example of a vessel selection panel which can beused to select a vessel such that the summary of atherosclerosisinformation is displayed on a per segment basis.

FIG. 9C illustrates an example of a panel that can be displayed on theuser interface which shows per segment atherosclerosis information.

FIG. 9D illustrates an example of a panel that can be displayed on theuser interface that contains stenosis per patient data.

FIG. 9E illustrates an example of a portion of a panel that can bedisplayed on the user interface that when a count is selected (e.g., byhovering over the number) segment details are displayed.

FIG. 9F illustrates an example of a portion of a panel that can bedisplayed on the user interface that shows stenosis per segment in agraphical format, for example, in a stenosis per segment bar graph.

FIG. 9G illustrates another example of a panel that can be displayed onthe user interface showing information of the vessel, for example,diameter stenosis and minimum luminal diameter.

FIG. 9H illustrates an example of a portion of a panel that can bedisplayed on the user interface indicating a diameter stenosis legend.

FIG. 9I illustrates an example of a panel that can be displayed on theuser interface indicating minimum and reference lumen diameters.

FIG. 9J illustrates a portion of the panel shown in FIG. 9I, and showshow specific minimum lumen diameter details can be quickly andefficiently displayed by selecting (e.g., by hovering over) a desiredgraphic of a lumen.

FIG. 9K illustrates an example of a panel that can be displayed in userinterface indicating CADS-RADS score selection.

FIG. 9L illustrates an example of a panel that can be displayed in theuser interface showing further CAD-RADS details generated in theanalysis.

FIG. 9M illustrates an example of a panel that can be displayed in theuser interface showing a table indicating quantitative stenosis andvessel outputs which are determined during the analysis.

FIG. 9N illustrates an example of a panel that can be displayed in theuser interface showing a table indicating quantitative plaque outputs.

FIG. 10 is a flowchart illustrating a process 1000 for analyzing anddisplaying CT images and corresponding information.

FIGS. 11A and 11B are example CT images illustrating how plaque canappear differently depending on the image acquisition parameters used tocapture the CT images. FIG. 11A illustrates a CT image reconstructedusing filtered back projection, while FIG. 11B illustrates the same CTimage reconstructed using iterative reconstruction.

FIGS. 11C and 11D provide another example that illustrates that plaquecan appear differently in CT images depending on the image acquisitionparameters used to capture the CT images. FIG. 11C illustrates a CTimage reconstructed by using iterative reconstruction, while FIG. 11Dillustrates the same image reconstructed using machine learning.

FIG. 12A is a block diagram representative of an embodiment of anormalization device that can be configured to normalize medical imagesfor use with the methods and systems described herein.

FIG. 12B is a perspective view of an embodiment of a normalizationdevice including a multilayer substrate.

FIG. 12C is a cross-sectional view of the normalization device of FIG.12B illustrating various compartments positioned therein for holdingsamples of known materials for use during normalization.

FIG. 12D illustrates a top down view of an example arrangement of aplurality of compartments within a normalization device. In theillustrated embodiment, the plurality of compartments are arranged in arectangular or grid-like pattern.

FIG. 12E illustrates a top down view of another example arrangement of aplurality of compartments within a normalization device. In theillustrated embodiment, the plurality of compartments are arranged in acircular pattern.

FIG. 12F is a cross-sectional view of another embodiment of anormalization device illustrating various features thereof, includingadjacently arranged compartments, self-sealing fillable compartments,and compartments of various sizes.

FIG. 12G is a perspective view illustrating an embodiment of anattachment mechanism for a normalization device that uses hook and loopfasteners to secure a substrate of the normalization device to afastener of the normalization device.

FIGS. 12H and 12I illustrate an embodiment of a normalization devicethat includes an indicator configured to indicate an expiration statusof the normalization device.

FIG. 12J is a flowchart illustrating an example method for normalizingmedical images for an algorithm-based medical imaging analysis, whereinnormalization of the medical images improves accuracy of thealgorithm-based medical imaging analysis.

FIG. 13 is a block diagram depicting an embodiment(s) of a system formedical image analysis, visualization, risk assessment, diseasetracking, treatment generation, and/or patient report generation.

FIG. 14 is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of a system for medical image analysis, visualization, riskassessment, disease tracking, treatment generation, and/or patientreport generation.

DETAILED DESCRIPTION

Although several embodiments, examples, and illustrations are disclosedbelow, it will be understood by those of ordinary skill in the art thatthe inventions described herein extend beyond the specifically disclosedembodiments, examples, and illustrations and includes other uses of theinventions and obvious modifications and equivalents thereof.Embodiments of the inventions are described with reference to theaccompanying figures, wherein like numerals refer to like elementsthroughout. The terminology used in the description presented herein isnot intended to be interpreted in any limited or restrictive mannersimply because it is being used in conjunction with a detaileddescription of certain specific embodiments of the inventions. Inaddition, embodiments of the inventions can comprise several novelfeatures and no single feature is solely responsible for its desirableattributes or is essential to practicing the inventions hereindescribed.

INTRODUCTION

Disclosed herein are systems, methods, and devices for medical imageanalysis, diagnosis, risk stratification, decision making and/or diseasetracking. Coronary heart disease affects over 17.6 million Americans.The current trend in treating cardiovascular health issues is generallytwo-fold. First, physicians generally review a patient's cardiovascularhealth from a macro level, for example, by analyzing the biochemistry orblood content or biomarkers of a patient to determine whether there arehigh levels of cholesterol elements in the bloodstream of a patient. Inresponse to high levels of cholesterol, some physicians will prescribeone or more drugs, such as statins, as part of a treatment plan in orderto decrease what is perceived as high levels of cholesterol elements inthe bloodstream of the patient.

The second general trend for currently treating cardiovascular healthissues involves physicians evaluating a patient's cardiovascular healththrough the use of angiography to identify large blockages in variousarteries of a patient. In response to finding large blockages in variousarteries, physicians in some cases will perform an angioplasty procedurewherein a balloon catheter is guided to the point of narrowing in thevessel. After properly positioned, the balloon is inflated to compressor flatten the plaque or fatty matter into the artery wall and/or tostretch the artery open to increase the flow of blood through the vesseland/or to the heart. In some cases, the balloon is used to position andexpand a stent within the vessel to compress the plaque and/or maintainthe opening of the vessel to allow more blood to flow. About 500,000heart stent procedures are performed each year in the United States.

However, a recent federally funded $100 million study calls intoquestion whether the current trends in treating cardiovascular diseaseare the most effective treatment for all types of patients. The recentstudy involved over 5,000 patients with moderate to severe stable heartdisease from 320 sites in 37 countries and provided new evidence showingthat stents and bypass surgical procedures are likely no more effectivethan drugs combined with lifestyle changes for people with stable heartdisease. Accordingly, it may be more advantageous for patients withstable heart disease to forgo invasive surgical procedures, such asangioplasty and/or heart bypass, and instead be prescribed heartmedicines, such as statins, and certain lifestyle changes, such asregular exercise. This new treatment regimen could affect thousands ofpatients worldwide. Of the estimated 500,000 heart stent proceduresperformed annually in the United States, it is estimated that a fifth ofthose are for people with stable heart disease. It is further estimatedthat 25% of the estimated 100,000 people with stable heart disease, orroughly 23,000 people, are individuals that do not experience any chestpain. Accordingly, over 20,000 patients annually could potentially forgoinvasive surgical procedures or the complications resulting from suchprocedures.

To determine whether a patient should forego invasive surgicalprocedures and opt instead for a drug regimen and/or to generate a moreeffective treatment plan, it can be important to more fully understandthe cardiovascular disease of a patient. Specifically, it can beadvantageous to better understand the arterial vessel health of apatient. For example, it is helpful to understand whether plaquebuild-up in a patient is mostly fatty matter build-up or mostlycalcified matter build-up, because the former situation may warranttreatment with heart medicines, such as statins, whereas in the lattersituation a patient should be subject to further periodic monitoringwithout prescribing heart medicine or implanting any stents. However, ifthe plaque build-up is significant enough to cause severe stenosis ornarrowing of the arterial vessel such that blood flow to heart musclemight be blocked, then an invasive angioplasty procedure to implant astent may likely be required because heart attack or sudden cardiacdeath (SCD) could occur in such patients without the implantation of astent to enlarge the vessel opening. Sudden cardiac death is one of thelargest causes of natural death in the United States, accounting forapproximately 325,000 adult deaths per year and responsible for nearlyhalf of all deaths from cardiovascular disease. For males, SCD is twiceas common as compared to females. In general, SCD strikes people in themid-30 to mid-40 age range. In over 50% of cases, sudden cardiac arrestoccurs with no warning signs.

With respect to the millions suffering from heart disease, there is aneed to better understand the overall health of the artery vesselswithin a patient beyond just knowing the blood chemistry or content ofthe blood flowing through such artery vessels. For example, in someembodiments of systems, devices, and methods disclosed herein, arterieswith “good” or stable plaque or plaque comprising hardened calcifiedcontent are considered non-life threatening to patients whereas arteriescontaining “bad” or unstable plaque or plaque comprising fatty materialare considered more life threatening because such bad plaque may rupturewithin arteries thereby releasing such fatty material into the arteries.Such a fatty material release in the blood stream can cause inflammationthat may result in a blood clot. A blood clot within an artery canprevent blood from traveling to heart muscle thereby causing a heartattack or other cardiac event. Further, in some instances, it isgenerally more difficult for blood to flow through fatty plaque buildupthan it is for blood to flow through calcified plaque build-up.Therefore, there is a need for better understanding and analysis of thearterial vessel walls of a patient.

Further, while blood tests and drug treatment regimens are helpful inreducing cardiovascular health issues and mitigating againstcardiovascular events (for example, heart attacks), such treatmentmethodologies are not complete or perfect in that such treatments canmisidentify and/or fail to pinpoint or diagnose significantcardiovascular risk areas. For example, the mere analysis of the bloodchemistry of a patient will not likely identify that a patient hasartery vessels having significant amounts of fatty deposit material badplaque buildup along a vessel wall. Similarly, an angiogram, whilehelpful in identifying areas of stenosis or vessel narrowing, may not beable to clearly identify areas of the artery vessel wall where there issignificant buildup of bad plaque. Such areas of buildup of bad plaquewithin an artery vessel wall can be indicators of a patient at high riskof suffering a cardiovascular event, such as a heart attack. In certaincircumstances, areas where there exist areas of bad plaque can lead to arupture wherein there is a release of the fatty materials into thebloodstream of the artery, which in turn can cause a clot to develop inthe artery. A blood clot in the artery can cause a stoppage of bloodflow to the heart tissue, which can result in a heart attack.Accordingly, there is a need for new technology for analyzing arteryvessel walls and/or identifying areas within artery vessel walls thatcomprise a buildup of plaque whether it be bad or otherwise.

Various systems, methods, and devices disclosed herein are directed toembodiments for addressing the foregoing issues. In particular, variousembodiments described herein relate to systems, methods, and devices formedical image analysis, diagnosis, risk stratification, decision makingand/or disease tracking. In some embodiments, the systems, devices, andmethods described herein are configured to utilize non-invasive medicalimaging technologies, such as a CT image for example, which can beinputted into a computer system configured to automatically and/ordynamically analyze the medical image to identify one or more coronaryarteries and/or plaque within the same. For example, in someembodiments, the system can be configured to utilize one or more machinelearning and/or artificial intelligence algorithms to automaticallyand/or dynamically analyze a medical image to identify, quantify, and/orclassify one or more coronary arteries and/or plaque. In someembodiments, the system can be further configured to utilize theidentified, quantified, and/or classified one or more coronary arteriesand/or plaque to generate a treatment plan, track disease progression,and/or a patient-specific medical report, for example using one or moreartificial intelligence and/or machine learning algorithms. In someembodiments, the system can be further configured to dynamically and/orautomatically generate a visualization of the identified, quantified,and/or classified one or more coronary arteries and/or plaque, forexample in the form of a graphical user interface. Further, in someembodiments, to calibrate medical images obtained from different medicalimaging scanners and/or different scan parameters or environments, thesystem can be configured to utilize a normalization device comprisingone or more compartments of one or more materials.

As will be discussed in further detail, the systems, devices, andmethods described herein allow for automatic and/or dynamic quantifiedanalysis of various parameters relating to plaque, cardiovasculararteries, and/or other structures. More specifically, in someembodiments described herein, a medical image of a patient, such as acoronary CT image, can be taken at a medical facility. Rather thanhaving a physician eyeball or make a general assessment of the patient,the medical image is transmitted to a backend main server in someembodiments that is configured to conduct one or more analyses thereofin a reproducible manner. As such, in some embodiments, the systems,methods, and devices described herein can provide a quantifiedmeasurement of one or more features of a coronary CT image usingautomated and/or dynamic processes. For example, in some embodiments,the main server system can be configured to identify one or morevessels, plaque, and/or fat from a medical image. Based on theidentified features, in some embodiments, the system can be configuredto generate one or more quantified measurements from a raw medicalimage, such as for example radiodensity of one or more regions ofplaque, identification of stable plaque and/or unstable plaque, volumesthereof, surface areas thereof, geometric shapes, heterogeneity thereof,and/or the like. In some embodiments, the system can also generate oneor more quantified measurements of vessels from the raw medical image,such as for example diameter, volume, morphology, and/or the like. Basedon the identified features and/or quantified measurements, in someembodiments, the system can be configured to generate a risk assessmentand/or track the progression of a plaque-based disease or condition,such as for example atherosclerosis, stenosis, and/or ischemia, usingraw medical images. Further, in some embodiments, the system can beconfigured to generate a visualization of GUI of one or more identifiedfeatures and/or quantified measurements, such as a quantized colormapping of different features. In some embodiments, the systems,devices, and methods described herein are configured to utilize medicalimage-based processing to assess for a subject his or her risk of acardiovascular event, major adverse cardiovascular event (MACE), rapidplaque progression, and/or non-response to medication. In particular, insome embodiments, the system can be configured to automatically and/ordynamically assess such health risk of a subject by analyzing onlynon-invasively obtained medical images. In some embodiments, one or moreof the processes can be automated using an AI and/or ML algorithm. Insome embodiments, one or more of the processes described herein can beperformed within minutes in a reproducible manner. This is starkcontrast to existing measures today which do not produce reproducibleprognosis or assessment, take extensive amounts of time, and/or requireinvasive procedures.

As such, in some embodiments, the systems, devices, and methodsdescribed herein are able to provide physicians and/or patients specificquantified and/or measured data relating to a patient's plaque that donot exist today. For example, in some embodiments, the system canprovide a specific numerical value for the volume of stable and/orunstable plaque, the ratio thereof against the total vessel volume,percentage of stenosis, and/or the like, using for example radiodensityvalues of pixels and/or regions within a medical image. In someembodiments, such detailed level of quantified plaque parameters fromimage processing and downstream analytical results can provide moreaccurate and useful tools for assessing the health and/or risk ofpatients in completely novel ways.

General Overview

In some embodiments, the systems, devices, and methods described hereinare configured to automatically and/or dynamically perform medical imageanalysis, diagnosis, risk stratification, decision making and/or diseasetracking. FIG. 1 is a flowchart illustrating an overview of an exampleembodiment(s) of a method for medical image analysis, visualization,risk assessment, disease tracking, treatment generation, and/or patientreport generation. As illustrated in FIG. 1, in some embodiments, thesystem is configured to access and/or analyze one or more medical imagesof a subject, such as for example a medical image of a coronary regionof a subject or patient.

In some embodiments, before obtaining the medical image, a normalizationdevice is attached to the subject and/or is placed within a field ofview of a medical imaging scanner at block 102. For example, in someembodiments, the normalization device can comprise one or morecompartments comprising one or more materials, such as water, calcium,and/or the like. Additional detail regarding the normalization device isprovided below. Medical imaging scanners may produce images withdifferent scalable radiodensities for the same object. This, forexample, can depend not only on the type of medical imaging scanner orequipment used but also on the scan parameters and/or environment of theparticular day and/or time when the scan was taken. As a result, even iftwo different scans were taken of the same subject, the brightnessand/or darkness of the resulting medical image may be different, whichcan result in less than accurate analysis results processed from thatimage. To account for such differences, in some embodiments, anormalization device comprising one or more known elements is scannedtogether with the subject, and the resulting image of the one or moreknown elements can be used as a basis for translating, converting,and/or normalizing the resulting image. As such, in some embodiments, anormalization device is attached to the subject and/or placed within thefield of view of a medical imaging scan at a medical facility.

In some embodiments, at block 104, the medical facility then obtains oneor more medical images of the subject. For example, the medical imagecan be of the coronary region of the subject or patient. In someembodiments, the systems disclosed herein can be configured to take inCT data from the image domain or the projection domain as raw scanneddata or any other medical data, such as but not limited to: x-ray;Dual-Energy Computed Tomography (DECT), Spectral CT, photon-countingdetector CT, ultrasound, such as echocardiography or intravascularultrasound (IVUS); magnetic resonance (MR) imaging; optical coherencetomography (OCT); nuclear medicine imaging, including positron-emissiontomography (PET) and single photon emission computed tomography (SPECT);near-field infrared spectroscopy (NIRS); and/or the like. As usedherein, the term CT image data or CT scanned data can be substitutedwith any of the foregoing medical scanning modalities and process suchdata through an artificial intelligence (AI) algorithm system in orderto generate processed CT image data. In some embodiments, the data fromthese imaging modalities enables determination of cardiovascularphenotype, and can include the image domain data, the projection domaindata, and/or a combination of both.

In some embodiments, at block 106, the medical facility can also obtainnon-imaging data from the subject. For example, this can include bloodtests, biomarkers, panomics and/or the like. In some embodiments, atblock 108, the medical facility can transmit the one or more medicalimages and/or other non-imaging data at block 108 to a main serversystem. In some embodiments, the main server system can be configured toreceive and/or otherwise access the medical image and/or othernon-imaging data at block 110.

In some embodiments, at block 112, the system can be configured toautomatically and/or dynamically analyze the one or more medical imageswhich can be stored and/or accessed from a medical image database 100.For example, in some embodiments, the system can be configured to takein raw CT image data and apply an artificial intelligence (AI)algorithm, machine learning (ML) algorithm, and/or other physics-basedalgorithm to the raw CT data in order to identify, measure, and/oranalyze various aspects of the identified arteries within the CT data.In some embodiments, the inputting of the raw medical image datainvolves uploading the raw medical image data into cloud-based datarepository system. In some embodiments, the processing of the medicalimage data involves processing the data in a cloud-based computingsystem using an AI and/or ML algorithm. In some embodiments, the systemcan be configured to analyze the raw CT data in about 1 minute, about 2minutes, about 3 minutes, about 4 minutes, about 5 minutes, about 6minutes, about 7 minutes, about 8 minutes, about 9 minutes, about 10minutes, about 15 minutes, about 20 minutes, about 30 minutes, about 35minutes, about 40 minutes, about 45 minutes, about 50 minutes, about 55minutes, about 60 minutes, and/or within a range defined by two of theaforementioned values.

In some embodiments, the system can be configured to utilize a vesselidentification algorithm to identify and/or analyze one or more vesselswithin the medical image. In some embodiments, the system can beconfigured to utilize a coronary artery identification algorithm toidentify and/or analyze one or more coronary arteries within the medicalimage. In some embodiments, the system can be configured to utilize aplaque identification algorithm to identify and/or analyze one or moreregions of plaque within the medical image. In some embodiments, thevessel identification algorithm, coronary artery identificationalgorithm, and/or plaque identification algorithm comprises an AI and/orML algorithm. For example, in some embodiments, the vesselidentification algorithm, coronary artery identification algorithm,and/or plaque identification algorithm can be trained on a plurality ofmedical images wherein one or more vessels, coronary arteries, and/orregions of plaque are pre-identified. Based on such training, forexample by use of a Convolutional Neural Network in some embodiments,the system can be configured to automatically and/or dynamicallyidentify from raw medical images the presence and/or parameters ofvessels, coronary arteries, and/or plaque.

As such, in some embodiments, the processing of the medical image or rawCT scan data can comprise analysis of the medical image or CT data inorder to determine and/or identify the existence and/or nonexistence ofcertain artery vessels in a patient. As a natural occurring phenomenon,certain arteries may be present in certain patients whereas such certainarteries may not exist in other patients.

In some embodiments, at block 112, the system can be further configuredto analyze the identified vessels, coronary arteries, and/or plaque, forexample using an AI and/or ML, algorithm. In particular, in someembodiments, the system can be configured to determine one or morevascular morphology parameters, such as for example arterial remodeling,curvature, volume, width, diameter, length, and/or the like. In someembodiments, the system can be configured to determine one or moreplaque parameters, such as for example volume, surface area, geometry,radiodensity, ratio or function of volume to surface area, heterogeneityindex, and/or the like of one or more regions of plaque shown within themedical image. “Radiodensity” as used herein is a broad term that refersto the relative inability of electromagnetic relation (e.g., X-rays) topass through a material. In reference to an image, radiodensity valuesrefer to values indicting a density in image data (e.g., film, print, orin an electronic format) where the radiodensity values in the imagecorresponds to the density of material depicted in the image.

In some embodiments, at block 114, the system can be configured toutilize the identified and/or analyzed vessels, coronary arteries,and/or plaque from the medical image to perform a point-in-time analysisof the subject. In some embodiments, the system can be configured to useautomatic and/or dynamic image processing of one or more medical imagestaken from one point in time to identify and/or analyze one or morevessels, coronary arteries, and/or plaque and derive one or moreparameters and/or classifications thereof. For example, as will bedescribed in more detail herein, in some embodiments, the system can beconfigured to generate one or more quantification metrics of plaqueand/or classify the identified regions of plaque as good or bad plaque.Further, in some embodiments, at block 114, the system can be configuredto generate one or more treatment plans for the subject based on theanalysis results. In some embodiments, the system can be configured toutilize one or more AI and/or ML algorithms to identify and/or analyzevessels or plaque, derive one or more quantification metrics and/orclassifications, and/or generate a treatment plan.

In some embodiments, if a previous scan or medical image of the subjectexists, the system can be configured to perform at block 126 one or moretime-based analyses, such as disease tracking. For example, in someembodiments, if the system has access to one or more quantifiedparameters or classifications derived from previous scans or medicalimages of the subject, the system can be configured to compare the samewith one or more quantified parameters or classifications derived from acurrent scan or medical image to determine the progression of diseaseand/or state of the subject.

In some embodiments, at block 116, the system is configured toautomatically and/or dynamically generate a Graphical User Interface(GUI) or other visualization of the analysis results at block 116, whichcan include for example identified vessels, regions of plaque, coronaryarteries, quantified metrics or parameters, risk assessment, proposedtreatment plan, and/or any other analysis result discussed herein. Insome embodiments, the system is configured to analyze arteries presentin the CT scan data and display various views of the arteries present inthe patient, for example within 10-15 minutes or less. In contrast, asan example, conducting a visual assessment of a CT to identify stenosisalone, without consideration of good or bad plaque or any other factor,can take anywhere between 15 minutes to more than an hour depending onthe skill level, and can also have substantial variability acrossradiologists and/or cardiac imagers.

In some embodiments, at block 118, the system can be configured totransmit the generated GUI or other visualization, analysis results,and/or treatment to the medical facility. In some embodiments, at block120, a physician at the medical facility can then review and/or confirmand/or revise the generated GUI or other visualization, analysisresults, and/or treatment.

In some embodiments, at block 122, the system can be configured tofurther generate and transmit a patient-specific medical report to apatient, who can receive the same at block 124. In some embodiments, thepatient-specific medical report can be dynamically generated based onthe analysis results derived from and/or other generated from themedical image processing and analytics. For example, thepatient-specific report can include identified vessels, regions ofplaque, coronary arteries, quantified metrics or parameters, riskassessment, proposed treatment plan, and/or any other analysis resultdiscussed herein.

In some embodiments, one or more of the process illustrated in FIG. 1can be repeated, for example for the same patient at a different time totrack progression of a disease and/or the state of the patient.

Image Processing-Based Classification of Good v. Bad Plaque

As discussed, in some embodiments, the systems, methods, and devicesdescribed herein are configured to automatically and/or dynamicallyidentify and/or classify good v. bad plaque or stable v. unstable plaquebased on medical image analysis and/or processing. For example, in someembodiments, the system can be configured to utilize an AI and/or MLalgorithm to identify areas in an artery that exhibit plaque buildupwithin, along, inside and/or outside the arteries. In some embodiments,the system can be configured to identify the outline or boundary ofplaque buildup associated with an artery vessel wall. In someembodiments, the system can be configured to draw or generate a linethat outlines the shape and configuration of the plaque buildupassociated with the artery. In some embodiments, the system can beconfigured to identify whether the plaque buildup is a certain kind ofplaque and/or the composition or characterization of a particular plaquebuildup. In some embodiments, the system can be configured tocharacterize plaque binarily, ordinally and/or continuously. In someembodiments, the system can be configured to determine that the kind ofplaque buildup identified is a “bad” kind of plaque due to the darkcolor or dark gray scale nature of the image corresponding to the plaquearea, and/or by determination of its attenuation density (e.g., using aHounsfield unit scale or other). For example, in some embodiments, thesystem can be configured to identify certain plaque as “bad” plaque ifthe brightness of the plaque is darker than a pre-determined level. Insome embodiments, the system can be configured to identify good plaqueareas based on the white coloration and/or the light gray scale natureof the area corresponding to the plaque buildup. For example, in someembodiments, the system can be configured to identify certain plaque as“good” plaque if the brightness of the plaque is lighter than apre-determined level. In some embodiments, the system can be configuredto determine that dark areas in the CT scan are related to “bad” plaque,whereas the system can be configured to identify good plaque areascorresponding to white areas. In some embodiments, the system can beconfigured to identify and determine the total area and/or volume oftotal plaque, good plaque, and/or bad plaque identified within an arteryvessel or plurality of vessels. In some embodiments, the system can beconfigured to determine the length of the total plaque area, good plaquearea, and/or bad plaque area identified. In some embodiments, the systemcan be configured to determine the width of the total plaque area, goodplaque area, and/or bad plaque area identified. The “good” plaque may beconsidered as such because it is less likely to cause heart attack, lesslikely to exhibit significant plaque progression, and/or less likely tobe ischemia, amongst others. Conversely, the “bad” plaque be consideredas such because it is more likely to cause heart attack, more likely toexhibit significant plaque progression, and/or more likely to beischemia, amongst others. In some embodiments, the “good” plaque may beconsidered as such because it is less likely to result in the no-reflowphenomenon at the time of coronary revascularization. Conversely, the“bad” plaque may be considered as such because it is more likely tocause the no-reflow phenomenon at the time of coronaryrevascularization.

FIG. 2A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for analysis and classification of plaque froma medical image, which can be obtained non-invasively. As illustrated inFIG. 2A, at block 202, in some embodiments, the system can be configuredto access a medical image, which can include a coronary region of asubject and/or be stored in a medical image database 100. The medicalimage database 100 can be locally accessible by the system and/or can belocated remotely and accessible through a network connection. Themedical image can comprise an image obtain using one or more modalitiessuch as for example, CT, Dual-Energy Computed Tomography (DECT),Spectral CT, photon-counting CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), Magnetic Resonance (MR) imaging,optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS). In someembodiments, the medical image comprises one or more of acontrast-enhanced CT image, non-contrast CT image, MR image, and/or animage obtained using any of the modalities described above.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 204, thesystem can be configured to identify one or more arteries. The one ormore arteries can include coronary arteries, carotid arteries, aorta,renal artery, lower extremity artery, upper extremity artery, and/orcerebral artery, amongst others. In some embodiments, the system can beconfigured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically identify one or more arteries orcoronary arteries using image processing. For example, in someembodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich arteries or coronary arteries have been identified, therebyallowing the AI and/or ML algorithm automatically identify arteries orcoronary arteries directly from a medical image. In some embodiments,the arteries or coronary arteries are identified by size and/orlocation.

In some embodiments, at block 206, the system can be configured toidentify one or more regions of plaque in the medical image. In someembodiments, the system can be configured to utilize one or more AIand/or ML algorithms to automatically and/or dynamically identify one ormore regions of plaque using image processing. For example, in someembodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich regions of plaque have been identified, thereby allowing the AIand/or ML algorithm automatically identify regions of plaque directlyfrom a medical image. In some embodiments, the system can be configuredto identify a vessel wall and a lumen wall for each of the identifiedcoronary arteries in the medical image. In some embodiments, the systemis then configured to determine the volume in between the vessel walland the lumen wall as plaque. In some embodiments, the system can beconfigured to identify regions of plaque based on the radiodensityvalues typically associated with plaque, for example by setting apredetermined threshold or range of radiodensity values that aretypically associated with plaque with or without normalizing using anormalization device.

In some embodiments, the system is configured to automatically and/ordynamically determine one or more vascular morphology parameters and/orplaque parameters at block 208 from the medical image. In someembodiments, the one or more vascular morphology parameters and/orplaque parameters can comprise quantified parameters derived from themedical image. For example, in some embodiments, the system can beconfigured to utilize an AI and/or ML algorithm or other algorithm todetermine one or more vascular morphology parameters and/or plaqueparameters. As another example, in some embodiments, the system can beconfigured to determine one or more vascular morphology parameters, suchas classification of arterial remodeling due to plaque, which canfurther include positive arterial remodeling, negative arterialremodeling, and/or intermediate arterial remodeling. In someembodiments, the classification of arterial remodeling is determinedbased on a ratio of the largest vessel diameter at a region of plaque toa normal reference vessel diameter of the same region which can beretrieved from a normal database. In some embodiments, the system can beconfigured to classify arterial remodeling as positive when the ratio ofthe largest vessel diameter at a region of plaque to a normal referencevessel diameter of the same region is more than 1.1. In someembodiments, the system can be configured to classify arterialremodeling as negative when the ratio of the largest vessel diameter ata region of plaque to a normal reference vessel diameter is less than0.95. In some embodiments, the system can be configured to classifyarterial remodeling as intermediate when the ratio of the largest vesseldiameter at a region of plaque to a normal reference vessel diameter isbetween 0.95 and 1.1.

Further, as part of block 208, in some embodiments, the system can beconfigured to determine a geometry and/or volume of one or more regionsof plaque and/or one or more vessels or arteries at block 201. Forexample, the system can be configured to determine if the geometry of aparticular region of plaque is round or oblong or other shape. In someembodiments, the geometry of a region of plaque can be a factor inassessing the stability of the plaque. As another example, in someembodiments, the system can be configured to determine the curvature,diameter, length, volume, and/or any other parameters of a vessel orartery from the medical image.

In some embodiments, as part of block 208, the system can be configuredto determine a volume and/or surface area of a region of plaque and/or aratio or other function of volume to surface area of a region of plaqueat block 203, such as for example a diameter, radius, and/or thicknessof a region of plaque. In some embodiments, a plaque having a low ratioof volume to surface area can indicate that the plaque is stable. Assuch, in some embodiments, the system can be configured to determinethat a ratio of volume to surface area of a region of plaque below apredetermined threshold is indicative of stable plaque.

In some embodiments, as part of block 208, the system can be configuredto determine a heterogeneity index of a region of plaque at block 205.For instance, in some embodiments, a plaque having a low heterogeneityor high homogeneity can indicate that the plaque is stable. As such, insome embodiments, the system can be configured to determine that aheterogeneity of a region of plaque below a predetermined threshold isindicative of stable plaque. In some embodiments, heterogeneity orhomogeneity of a region of plaque can be determined based on theheterogeneity or homogeneity of radiodensity values within the region ofplaque. As such, in some embodiments, the system can be configured todetermine a heterogeneity index of plaque by generating spatial mapping,such as a three-dimensional histogram, of radiodensity values within oracross a geometric shape or region of plaque. In some embodiments, if agradient or change in radiodensity values across the spatial mapping isabove a certain threshold, the system can be configured to assign a highheterogeneity index. Conversely, in some embodiments, if a gradient orchange in radiodensity values across the spatial mapping is below acertain threshold, the system can be configured to assign a lowheterogeneity index.

In some embodiments, as part of block 208, the system can be configuredto determine a radiodensity of plaque and/or a composition thereof atblock 207. For example, a high radiodensity value can indicate that aplaque is highly calcified or stable, whereas a low radiodensity valuecan indicate that a plaque is less calcified or unstable. As such, insome embodiments, the system can be configured to determine that aradiodensity of a region of plaque above a predetermined threshold isindicative of stable stabilized plaque. In addition, different areaswithin a region of plaque can be calcified at different levels andthereby show different radiodensity values. As such, in someembodiments, the system can be configured to determine the radiodensityvalues of a region of plaque and/or a composition or percentage orchange of radiodensity values within a region of plaque. For instance,in some embodiments, the system can be configured to determine how muchor what percentage of plaque within a region of plaque shows aradiodensity value within a low range, medium range, high range, and/orany other classification.

Similarly, in some embodiments, as part of block 208, the system can beconfigured to determine a ratio of radiodensity value of plaque to avolume of plaque at block 209. For instance, it can be important toassess whether a large or small region of plaque is showing a high orlow radiodensity value. As such, in some embodiments, the system can beconfigured to determine a percentage composition of plaque comprisingdifferent radiodensity values as a function or ratio of volume ofplaque.

In some embodiments, as part of block 208, the system can be configuredto determine the diffusivity and/or assign a diffusivity index to aregion of plaque at block 211. For example, in some embodiments, thediffusivity of a plaque can depend on the radiodensity value of plaque,in which a high radiodensity value can indicate low diffusivity orstability of the plaque.

In some embodiments, at block 210, the system can be configured toclassify one or regions of plaque identified from the medical image asstable v. unstable or good v. bad based on the one or more vascularmorphology parameters and/or quantified plaque parameters determinedand/or derived from raw medical images. In particular, in someembodiments, the system can be configured to generate a weighted measureof one or more vascular morphology parameters and/or quantified plaqueparameters determined and/or derived from raw medical images. Forexample, in some embodiments, the system can be configured weight one ormore vascular morphology parameters and/or quantified plaque parametersequally. In some embodiments, the system can be configured weight one ormore vascular morphology parameters and/or quantified plaque parametersdifferently. In some embodiments, the system can be configured weightone or more vascular morphology parameters and/or quantified plaqueparameters logarithmically, algebraically, and/or utilizing anothermathematical transform. In some embodiments, the system is configured toclassify one or more regions of plaque at block 210 using the generatedweighted measure and/or using only some of the vascular morphologyparameters and/or quantified plaque parameters.

In some embodiments, at block 212, the system is configured to generatea quantized color mapping based on the analyzed and/or determinedparameters. For example, in some embodiments, the system is configuredto generate a visualization of the analyzed medical image by generatinga quantized color mapping of calcified plaque, non-calcified plaque,good plaque, bad plaque, stable plaque, and/or unstable plaque asdetermined using any of the analytical techniques described herein.Further, in some embodiments, the quantified color mapping can alsoinclude arteries and/or epicardial fat, which can also be determined bythe system, for example by utilizing one or more AI and/or MLalgorithms.

In some embodiments, at block 214, the system is configured to generatea proposed treatment plan for the subject based on the analysis, such asfor example the classification of plaque derived automatically from araw medical image. In particular, in some embodiments, the system can beconfigured to assess or predict the risk of atherosclerosis, stenosis,and/or ischemia of the subject based on a raw medical image andautomated image processing thereof.

In some embodiments, one or more processes described herein inconnection with FIG. 2A can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for disease tracking and/or other purposes.

Determination of Non-Calcified Plaque from a Non-Contrast CT Image(s)

As discussed herein, in some embodiments, the system can be configuredto utilize a CT or other medical image of a subject as input forperforming one or more image analysis techniques to assess a subject,including for example risk of a cardiovascular event. In someembodiments, such CT image can comprise a contrast-enhanced CT image, inwhich case some of the analysis techniques described herein can bedirectly applied, for example to identify or classify plaque. However,in some embodiments, such CT image can comprise a non-contrast CT image,in which case it can be more difficult to identify and/or determinenon-calcified plaque due to its low radiodensity value and overlap withother low radiodensity values components, such as blood for example. Assuch, in some embodiments, the systems, devices, and methods describedherein provide a novel approach to determining non-calcified plaque froma non-contrast CT image, which can be more widely available.

Also, in some embodiments, in addition to or instead of analyzing acontrast-enhanced CT scan, the system can also be configured to examinethe attenuation densities within the arteries that are lower than theattenuation density of the blood flowing within them in a non-contrastCT scan. In some embodiments, these “low attenuation” plaques may bedifferentiated between the blood attenuation density and the fat thatsometimes surrounds the coronary artery and/or may representnon-calcified plaques of different materials. In some embodiments, thepresence of these non-calcified plaques may offer incremental predictionfor whether a previously calcified plaque is stabilizing or worsening orprogressing or regressing. These findings that are measurable throughthese embodiments may be linked to the prognosis of a patient, whereincalcium stabilization (that is, higher attenuation densities) and lackof non-calcified plaque by may associated with a favorable prognosis,while lack of calcium stabilization (that is, no increase in attenuationdensities), or significant progression or new calcium formation may beassociated with a poorer prognosis, including risk of rapid progressionof disease, heart attack or other major adverse cardiovascular event.

FIG. 2B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of non-calcified and/orlow-attenuated plaque from a medical image, such as a non-contrast CTimage. As discussed herein and as illustrated in FIG. 2B, in someembodiments, the system can be configured to determine non-calcifiedand/or low-attenuated plaque from a medical image. In some embodiments,the medical image can be of the coronary region of the subject orpatient. In some embodiments, the medical image can be obtained usingone or more modalities such as CT, Dual-Energy Computed Tomography(DECT), Spectral CT, x-ray, ultrasound, echocardiography, IVUS, MR, OCT,nuclear medicine imaging, PET, SPECT, NIRS, and/or the like. In someembodiments, the system can be configured to access one or more medicalimages at block 202, for example from a medical image database 100.

In some embodiments, in order to determine non-calcified and/orlow-attenuated plaque from the medical image or non-contrast CT image,the system can be configured to utilize a stepwise approach to firstidentify areas within the medical image that are clearly non-calcifiedplaque. In some embodiments, the system can then conduct a more detailedanalysis of the remaining areas in the image to identify other regionsof non-calcified and/or low-attenuated plaque. By utilizing suchcompartmentalized or a stepwise approach, in some embodiments, thesystem can identify or determine non-calcified and/or low-attenuatedplaque from the medical image or non-contrast CT image with a fasterturnaround rather than having to apply a more complicated analysis toevery region or pixel of the image.

In particular, in some embodiments, at block 224, the system can beconfigured to identify epicardial fat from the medical image. In someembodiments, the system can be configured to identify epicardial fat bydetermining every pixel or region within the image that has aradiodensity value below a predetermined threshold and/or within apredetermined range. The exact predetermined threshold value or range ofradiodensity for identifying epicardial fat can depend on the medicalimage, scanner type, scan parameters, and/or the like, which is why anormalization device can be used in some instances to normalize themedical image. For example, in some embodiments, the system can beconfigured to identify as epicardial fat pixels and/or regions withinthe medical image or non-contrast CT image with a radiodensity valuethat is around −100 Hounsfield units and/or within a range that includes−100 Hounsfield units. In particular, in some embodiments, the systemcan be configured to identify as epicardial fat pixels and/or regionswithin the medical image or non-contrast CT image with a radiodensityvalue that is within a range with a lower limit of about −100 Hounsfieldunits, about −110 Hounsfield units, about −120 Hounsfield units, about−130 Hounsfield units, about −140 Hounsfield units, about −150Hounsfield units, about −160 Hounsfield units, about −170 Hounsfieldunits, about −180 Hounsfield units, about −190 Hounsfield units, orabout −200 Hounsfield units, and an upper limit of about 30 Hounsfieldunits, about 20 Hounsfield units, about 10 Hounsfield units, about 0Hounsfield units, about −10 Hounsfield units, about −20 Hounsfieldunits, about −30 Hounsfield units, about −40 Hounsfield units, about −50Hounsfield units, about −60 Hounsfield units, about −70 Hounsfieldunits, about −80 Hounsfield units, or about −90 Hounsfield units.

In some embodiments, the system can be configured to identify and/orsegment arteries on the medical image or non-contrast CT image using theidentified epicardial fat as outer boundaries of the arteries. Forexample, the system can be configured to first identify regions ofepicardial fat on the medical image and assign a volume in betweenepicardial fat as an artery, such as a coronary artery.

In some embodiments, at block 226, the system can be configured toidentify a first set of pixels or regions within the medical image, suchas within the identified arteries, as non-calcified or low-attenuatedplaque. More specifically, in some embodiments, the system can beconfigured to identify as an initial set low-attenuated or non-calcifiedplaque by identifying pixels or regions with a radiodensity value thatis below a predetermined threshold or within a predetermined range. Forexample, the predetermined threshold or predetermined range can be setsuch that the resulting pixels can be confidently marked aslow-attenuated or non-calcified plaque without likelihood of confusionwith another matter such as blood. In particular, in some embodiments,the system can be configured to identify the initial set oflow-attenuated or non-calcified plaque by identifying pixels or regionswith a radiodensity value below around 30 Hounsfield units. In someembodiments, the system can be configured to identify the initial set oflow-attenuated or non-calcified plaque by identifying pixels or regionswith a radiodensity value at or below around 60 Hounsfield units, around55 Hounsfield units, around 50 Hounsfield units, around 45 Hounsfieldunits, around 40 Hounsfield units, around 35 Hounsfield units, around 30Hounsfield units, around 25 Hounsfield units, around 20 Hounsfieldunits, around 15 Hounsfield units, around 10 Hounsfield units, around 5Hounsfield units, and/or with a radiodensity value at or above around 0Hounsfield units, around 5 Hounsfield units, around 10 Hounsfield units,around 15 Hounsfield units, around 20 Hounsfield units, around 25Hounsfield units, and/or around 30 Hounsfield units. In someembodiments, the system can be configured classify pixels or regionsthat fall within or below this predetermined range of radiodensityvalues as a first set of identified non-calcified or low-attenuatedplaque at block 238.

In some embodiments, the system at block 228 can be configured toidentify a second set of pixels or regions within the medical image,such as within the identified arteries, that may or may not representlow-attenuated or non-calcified plaque. As discussed, in someembodiments, this second set of candidates of pixels or regions mayrequire additional analysis to confirm that they represent plaque. Inparticular, in some embodiments, the system can be configured toidentify this second set of pixels or regions that may potentially below-attenuated or non-calcified plaque by identifying pixels or regionsof the image with a radiodensity value within a predetermined range. Insome embodiments, the predetermined range for identifying this secondset of pixels or regions can be between around 30 Hounsfield units and100 Hounsfield units. In some embodiments, the predetermined range foridentifying this second set of pixels or regions can have a lower limitof around 0 Hounsfield units, 5 Hounsfield units, 10 Hounsfield units,15 Hounsfield units, 20 Hounsfield units, 25 Hounsfield units, 30Hounsfield units, 35 Hounsfield units, 40 Hounsfield units, 45Hounsfield units, 50 Hounsfield units, and/or an upper limit of around55 Hounsfield units, 60 Hounsfield units, 65 Hounsfield units, 70Hounsfield units, 75 Hounsfield units, 80 Hounsfield units, 85Hounsfield units, 90 Hounsfield units, 95 Hounsfield units, 100Hounsfield units, 110 Hounsfield units, 120 Hounsfield units, 130Hounsfield units, 140 Hounsfield units, 150 Hounsfield units.

In some embodiments, at block 230, the system can be configured conductan analysis of the heterogeneity of the identified second set of pixelsor regions. For example, depending on the range of radiodensity valuesused to identify the second set of pixels, in some embodiments, thesecond set of pixels or regions may include blood and/or plaque. Bloodcan typically show a more homogeneous gradient of radiodensity valuescompared to plaque. As such, in some embodiments, by analyzing thehomogeneity or heterogeneity of the pixels or regions identified as partof the second set, the system can be able to distinguish between bloodand non-calcified or low attenuated plaque. As such, in someembodiments, the system can be configured to determine a heterogeneityindex of the second set of regions of pixels identified from the medicalimage by generating spatial mapping, such as a three-dimensionalhistogram, of radiodensity values within or across a geometric shape orregion of plaque. In some embodiments, if a gradient or change inradiodensity values across the spatial mapping is above a certainthreshold, the system can be configured to assign a high heterogeneityindex and/or classify as plaque. Conversely, in some embodiments, if agradient or change in radiodensity values across the spatial mapping isbelow a certain threshold, the system can be configured to assign a lowheterogeneity index and/or classify as blood.

In some embodiments, at block 240, the system can be configured toidentify a subset of the second set of regions of pixels identified fromthe medical image as plaque or non-calcified or low-attenuated plaque.In some embodiments, at block 242, the system can be configured tocombine the first set of identified non-calcified or low-attenuatedplaque from block 238 and the second set of identified non-calcified orlow-attenuated plaque from block 240. As such, even using non-contrastCT images, in some embodiments, the system can be configured to identifylow-attenuated or non-calcified plaque which can be more difficult toidentify compared to calcified or high-attenuated plaque due to possibleoverlap with other matter such as blood.

In some embodiments, the system can also be configured to determinecalcified or high-attenuated plaque from the medical image at block 232.This process can be more straightforward compared to identifyinglow-attenuated or non-calcified plaque from the medical image ornon-contrast CT image. In particular, in some embodiments, the systemcan be configured to identify calcified or high-attenuated plaque fromthe medical image or non-contrast CT image by identifying pixels orregions within the image that have a radiodensity value above apredetermined threshold and/or within a predetermined range. Forexample, in some embodiments, the system can be configured to identifyas calcified or high-attenuated plaque regions or pixels from themedical image or non-contrast CT image having a radiodensity value abovearound 100 Hounsfield units, around 150 Hounsfield units, around 200Hounsfield units, around 250 Hounsfield units, around 300 Hounsfieldunits, around 350 Hounsfield units, around 400 Hounsfield units, around450 Hounsfield units, around 500 Hounsfield units, around 600 Hounsfieldunits, around 700 Hounsfield units, around 800 Hounsfield units, around900 Hounsfield units, around 1000 Hounsfield units, around 1100Hounsfield units, around 1200 Hounsfield units, around 1300 Hounsfieldunits, around 1400 Hounsfield units, around 1500 Hounsfield units,around 1600 Hounsfield units, around 1700 Hounsfield units, around 1800Hounsfield units, around 1900 Hounsfield units, around 2000 Hounsfieldunits, around 2500 Hounsfield units, around 3000 Hounsfield units,and/or any other minimum threshold.

In some embodiments, at block 234, the system can be configured togenerate a quantized color mapping of one or more identified mattersfrom the medical image. For example, in some embodiments, the system canbe configured assign different colors to each of the different regionsassociated with different matters, such as non-calcified orlow-attenuated plaque, calcified or high-attenuated plaque, all plaque,arteries, epicardial fat, and/or the like. In some embodiments, thesystem can be configured to generate a visualization of the quantizedcolor map and/or present the same to a medical personnel or patient viaa GUI. In some embodiments, at block 236, the system can be configuredto generate a proposed treatment plan for a disease based on one or moreof the identified non-calcified or low-attenuated plaque, calcified orhigh-attenuated plaque, all plaque, arteries, epicardial fat, and/or thelike. For example, in some embodiments, the system can be configured togenerate a treatment plan for an arterial disease, renal artery disease,abdominal atherosclerosis, carotid atherosclerosis, and/or the like, andthe medical image being analyzed can be taken from any one or moreregions of the subject for such disease analysis.

In some embodiments, one or more processes described herein inconnection with FIG. 2B can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for disease tracking and/or other purposes.

Further, in some embodiments, the system can be configured to identifyand/or determine non-calcified plaque from a DECT or spectral CT image.Similar to the processes described above, in some embodiments, thesystem can be configured to access a DECT or spectral CT image, identifyepicardial fat on the DECT image or spectral CT and/or segment one ormore arteries on the DECT image or spectral CT, identify and/or classifya first set of pixels or regions within the arteries as a first set oflow-attenuated or non-calcified plaque, and/or identify a second set ofpixels or regions within the arteries as a second set of low-attenuatedor non-calcified plaque. However, unlike the techniques described above,in some embodiments, such as for example where a DECT or spectral CTimage is being analyzed, the system can be configured to identify asubset of those second set of pixels without having to perform aheterogeneity and/or homogeneity analysis of the second set of pixels.Rather, in some embodiments, the system can be configured to distinguishbetween blood and low-attenuated or non-calcified plaque directly fromthe image, for example by utilizing the dual or multispectral aspect ofa DECT or spectral CT image. In some embodiments, the system can beconfigured to combine the first set of identified pixels or regions andthe subset of the second set of pixels or regions identified aslow-attenuated or non-calcified plaque to identify a whole set of thesame on the medical image. In some embodiments, even if analyzing a DECTor spectral CT image, the system can be configured to further analyzethe second set of pixels or regions by performing a heterogeneity orhomogeneity analysis, similar to that described above in relation toblock 230. For example, even if analyzing a DECT or spectral CT image,in some embodiments, the distinction between certain areas of bloodand/or low-attenuated or non-calcified plaque may not be complete and/oraccurate.

Imaging Analysis-Based Risk Assessment

In some embodiments, the systems, devices, and methods described hereinare configured to utilize medical image-based processing to assess for asubject his or her risk of a cardiovascular event, major adversecardiovascular event (MACE), rapid plaque progression, and/ornon-response to medication. In particular, in some embodiments, thesystem can be configured to automatically and/or dynamically assess suchhealth risk of a subject by analyzing only non-invasively obtainedmedical images, for example using AI and/or ML algorithms, to provide afull image-based analysis report within minutes.

In particular, in some embodiments, the system can be configured tocalculate the total amount of plaque (and/or amounts of specific typesof plaque) within a specific artery and/or within all the arteries of apatient. In some embodiments, the system can be configured to determinethe total amount of bad plaque in a particular artery and/or within atotal artery area across some or all of the arteries of the patient. Insome embodiments, the system can be configured to determine a riskfactor and/or a diagnosis for a particular patient to suffer a heartattack or other cardiac event based on the total amount of plaque in aparticular artery and/or a total artery area across some or all of thearteries of a patient. Other risk factors that can be determined fromthe amount of “bad” plaque, or the relative amount of “bad” versus“good” plaque, can include the rate of disease progression and/or thelikelihood of ischemia. In some embodiments, plaques can be measured bytotal volume (or area on cross-sectional imaging) as well as by relativeamount when normalized to the total vessel volumes, total vessel lengthsor subtended myocardium.

In some embodiments, the imaging data of the coronary arteries caninclude measures of atherosclerosis, stenosis and vascular morphology.In some embodiments, this information can be combined with othercardiovascular disease phenotyping by quantitative characterization ofleft and right ventricles, left and right atria; aortic, mitral,tricuspid and pulmonic valves; aorta, pulmonary artery, pulmonary vein,coronary sinus and inferior and superior vena cava; epicardial orpericoronary fat; lung densities; bone densities; pericardium andothers. As an example, in some embodiments, the imaging data for thecoronary arteries may be integrated with the left ventricular mass,which can be segmented according to the amount and location of theartery it is subtended by. This combination of left ventricularfractional myocardial mass to coronary artery information may enhancethe prediction of whether a future heart attack will be a large one or asmall one. As another example, in some embodiments, the vessel volume ofthe coronary arteries can be related to the left ventricular mass as ameasure of left ventricular hypertrophy, which can be a common findingin patients with hypertension. Increased left ventricular mass (relativeor absolute) may indicate disease worsening or uncontrolledhypertension. As another example, in some embodiments, the onset,progression, and/or worsening of atrial fibrillation may be predicted bythe atrial size, volume, atrial free wall mass and thickness, atrialfunction and fat surrounding the atrium. In some embodiments, thesepredictions may be done with a ML or AI algorithm or other algorithmtype.

Sequentially, in some embodiments, the algorithms that allow forsegmentation of atherosclerosis, stenosis and vascular morphology—alongwith those that allow for segmentation of other cardiovascularstructures, and thoracic structures—may serve as the inputs for theprognostic algorithms. In some embodiments, the outputs of theprognostic algorithms, or those that allow for image segmentation, maybe leveraged as inputs to other algorithms that may then guide clinicaldecision making by predicting future events. As an example, in someembodiments, the integrated scoring of atherosclerosis, stenosis, and/orvascular morphology may identify patients who may benefit from coronaryrevascularization, that is, those who will achieve symptom benefit,reduced risk of heart attack and death. As another example, in someembodiments, the integrated scoring of atherosclerosis, stenosis andvascular morphology may identify individuals who may benefit fromspecific types of medications, such as lipid lowering medications (suchas statin medications, PCSK-9 inhibitors, icosopent ethyl, and others);Lp(a) lowering medications; anti-thrombotic medications (such asclopidogrel, rivoroxaban and others). In some embodiments, the benefitthat is predicted by these algorithms may be for reduced progression,determination of type of plaque progression (progression, regression ormixed response), stabilization due to the medical therapy, and/or needfor heightened intensified therapy. In some embodiments, the imagingdata may be combined with other data to identify areas within a coronaryvessel that are normal and without plaque now but may be at higherlikelihood of future plaque formation.

In some embodiments, an automated or manual co-registration method canbe combined with the imaging segmentation data to compare two or moreimages over time. In some embodiments, the comparison of these imagescan allow for determination of differences in coronary arteryatherosclerosis, stenosis and vascular morphology over time, and can beused as an input variable for risk prediction.

In some embodiments, the imaging data of the coronary arteries foratherosclerosis, stenosis, and vascular morphology—coupled or notcoupled to thoracic and cardiovascular disease measurements—can beintegrated into an algorithm that determines whether a coronary vesselis ischemia, or exhibits reduced blood flow or pressure (either at restor hyperemic states).

In some embodiments, the algorithms for coronary atherosclerosis,stenosis and ischemia can be modified by a computer system and/or otherto remove plaque or “seal” plaque. In some embodiments, a comparison canbe made before or after the system has removed or sealed the plaque todetermine whether any changes have occurred. For example, in someembodiments, the system can be configured to determine whether coronaryischemia is removed with the plaque sealing.

In some embodiments, the characterization of coronary atherosclerosis,stenosis and/or vascular morphology can enable relating a patient'sbiological age to their vascular age, when compared to apopulation-based cohort of patients who have undergone similar scanning.As an example, a 60-year old patient may have X units of plaque in theircoronary arteries that is equivalent to the average 70-year old patientin the population-based cohort. In this case, the patient's vascular agemay be 10 years older than the patient's biological age.

In some embodiments, the risk assessment enabled by the imagesegmentation prediction algorithms can allow for refined measures ofdisease or death likelihood in people being considered for disability orlife insurance. In this scenario, the risk assessment may replace oraugment traditional actuarial algorithms.

In some embodiments, imaging data may be combined with other data toaugment risk assessment for future adverse events, such as heartattacks, strokes, death, rapid progression, non-response to medicaltherapy, no-reflow phenomenon and others. In some embodiments, otherdata may include a multi-omic approach wherein an algorithm integratesthe imaging phenotype data with genotype data, proteomic data,transcriptomic data, metabolomic data, microbiomic data and/or activityand lifestyle data as measured by a smart phone or similar device.

FIG. 3A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for risk assessment based on medical imageanalysis. As illustrated in FIG. 3A, in some embodiments, the system canbe configured to access a medical image at block 202. Further, in someembodiments, the system can be configured to identify one or morearteries at block 204 and/or one or more regions of plaque at block 206.In addition, in some embodiments, the system can be configured todetermine one or more vascular morphology and/or quantified plaqueparameters at block 208 and/or classify stable or unstable plaque basedon the determined one or more vascular morphology and/or quantifiedplaque parameters and/or a weighted measure thereof at block 210.Additional detail regarding the processes and techniques represented inblocks 202, 204, 206, 208, and 210 can be found in the description abovein relation to FIG. 2A.

In some embodiments, the system can automatically and/or dynamicallydetermine and/or generate a risk of cardiovascular event for the subjectat block 302, for example using the classified stable and/or unstableregions of plaque. More specifically, in some embodiments, the systemcan utilize an AI, ML, or other algorithm to generate a risk ofcardiovascular event, MACE, rapid plaque progression, and/ornon-response to medication at block 302 based on the image analysis.

In some embodiments, at block 304, the system can be configured tocompare the determined one or more vascular morphology parameters,quantified plaque parameters, and/or classified stable v. unstableplaque and/or values thereof, such as volume, ratio, and/or the like, toone or more known datasets of coronary values derived from one or moreother subjects. The one or more known datasets can comprise one or morevascular morphology parameters, quantified plaque parameters, and/orclassified stable v. unstable plaque and/or values thereof, such asvolume, ratio, and/or the like, derived from medical images taken fromother subjects, including healthy subjects and/or subjects with varyinglevels of risk. For example, the one or more known datasets of coronaryvalues can be stored in a coronary values database 306 that can belocally accessible by the system and/or remotely accessible via anetwork connection by the system.

In some embodiments, at block 308, the system can be configured toupdate the risk of cardiovascular event for the subject based on thecomparison to the one or more known datasets. For example, based on thecomparison, the system may increase or decrease the previously generatedrisk assessment. In some embodiments, the system may maintain thepreviously generated risk assessment even after comparison. In someembodiments, the system can be configured to generate a proposedtreatment for the subject based on the generated and/or updated riskassessment after comparison with the known datasets of coronary values.

In some embodiments, at block 310, the system can be configured tofurther identify one or more other cardiovascular structures from themedical image and/or determine one or more parameters associated withthe same. For example, the one or more additional cardiovascularstructures can include the left ventricle, right ventricle, left atrium,right atrium, aortic valve, mitral valve, tricuspid valve, pulmonicvalve, aorta, pulmonary artery, inferior and superior vena cava,epicardial fat, and/or pericardium.

In some embodiments, parameters associated with the left ventricle caninclude size, mass, volume, shape, eccentricity, surface area,thickness, and/or the like. Similarly, in some embodiments, parametersassociated with the right ventricle can include size, mass, volume,shape, eccentricity, surface area, thickness, and/or the like. In someembodiments, parameters associated with the left atrium can includesize, mass, volume, shape, eccentricity, surface area, thickness,pulmonary vein angulation, atrial appendage morphology, and/or the like.In some embodiments, parameters associated with the right atrium caninclude size, mass, volume, shape, eccentricity, surface area,thickness, and/or the like.

Further, in some embodiments, parameters associated with the aorticvalve can include thickness, volume, mass, calcifications,three-dimensional map of calcifications and density, eccentricity ofcalcification, classification by individual leaflet, and/or the like. Insome embodiments, parameters associated with the mitral valve caninclude thickness, volume, mass, calcifications, three-dimensional mapof calcifications and density, eccentricity of calcification,classification by individual leaflet, and/or the like. In someembodiments, parameters associated with the tricuspid valve can includethickness, volume, mass, calcifications, three-dimensional map ofcalcifications and density, eccentricity of calcification,classification by individual leaflet, and/or the like. In someembodiments, parameters associated with the pulmonic valve can includethickness, volume, mass, calcifications, three-dimensional map ofcalcifications and density, eccentricity of calcification,classification by individual leaflet, and/or the like.

In some embodiments, parameters associated with the aorta can includedimensions, volume, diameter, area, enlargement, outpouching, and/or thelike. In some embodiments, parameters associated with the pulmonaryartery can include dimensions, volume, diameter, area, enlargement,outpouching, and/or the like. In some embodiments, parameters associatedwith the inferior and superior vena cava can include dimensions, volume,diameter, area, enlargement, outpouching, and/or the like.

In some embodiments, parameters associated with epicardial fat caninclude volume, density, density in three dimensions, and/or the like.In some embodiments, parameters associated with the pericardium caninclude thickness, mass, and/or the like.

In some embodiments, at block 312, the system can be configured toclassify one or more of the other identified cardiovascular structures,for example using the one or more determined parameters thereof. In someembodiments, for one or more of the other identified cardiovascularstructures, the system can be configured to classify each as normal v.abnormal, increased or decreased, and/or static or dynamic over time.

In some embodiments, at block 314, the system can be configured tocompare the determined one or more parameters of other cardiovascularstructures to one or more known datasets of cardiovascular structureparameters derived from one or more other subjects. The one or moreknown datasets of cardiovascular structure parameters can include anyone or more of the parameters mentioned above associated with the othercardiovascular structures. In some embodiments, the cardiovascularstructure parameters of the one or more known datasets can be derivedfrom medical images taken from other subjects, including healthysubjects and/or subjects with varying levels of risk. In someembodiments, the one or more known datasets of cardiovascular structureparameters can be stored in a cardiovascular structure values orcardiovascular disease (CVD) database 316 that can be locally accessibleby the system and/or remotely accessible via a network connection by thesystem.

In some embodiments, at block 318, the system can be configured toupdate the risk of cardiovascular event for the subject based on thecomparison to the one or more known datasets of cardiovascular structureparameters. For example, based on the comparison, the system mayincrease or decrease the previously generated risk assessment. In someembodiments, the system may maintain the previously generated riskassessment even after comparison.

In some embodiments, at block 320, the system can be configured togenerate a quantified color map, which can include color coding for oneor more other cardiovascular structures identified from the medicalimage, stable plaque, unstable plaque, arteries, and/or the like. Insome embodiments, at block 322, the system can be configured to generatea proposed treatment for the subject based on the generated and/orupdated risk assessment after comparison with the known datasets ofcardiovascular structure parameters.

In some embodiments, at block 324, the system can be configured tofurther identify one or more non-cardiovascular structures from themedical image and/or determine one or more parameters associated withthe same. For example, the medical image can include one or morenon-cardiovascular structures that are in the field of view. Inparticular, the one or more non-cardiovascular structures can includethe lungs, bones, liver, and/or the like.

In some embodiments, parameters associated with the non-cardiovascularstructures can include volume, surface area, ratio or function of volumeto surface area, heterogeneity of radiodensity values, radiodensityvalues, geometry (such as oblong, spherical, and/or the like), spatialradiodensity, spatial scarring, and/or the like. In addition, in someembodiments, parameters associated with the lungs can include density,scarring, and/or the like. For example, in some embodiments, the systemcan be configured to associate a low Hounsfield unit of a region of thelungs with emphysema. In some embodiments, parameters associated withbones, such as the spine and/or ribs, can include radiodensity, presenceand/or extent of fractures, and/or the like. For example, in someembodiments, the system can be configured to associate a low Hounsfieldunit of a region of bones with osteoporosis. In some embodiments,parameters associated with the liver can include density fornon-alcoholic fatty liver disease which can be assessed by the system byanalyzing and/or comparing to the Hounsfield unit density of the liver.

In some embodiments, at block 326, the system can be configured toclassify one or more of the identified non-cardiovascular structures,for example using the one or more determined parameters thereof. In someembodiments, for one or more of the identified non-cardiovascularstructures, the system can be configured to classify each as normal v.abnormal, increased or decreased, and/or static or dynamic over time.

In some embodiments, at block 328, the system can be configured tocompare the determined one or more parameters of non-cardiovascularstructures to one or more known datasets of non-cardiovascular structureparameters or non-CVD values derived from one or more other subjects.The one or more known datasets of non-cardiovascular structureparameters or non-CVD values can include any one or more of theparameters mentioned above associated with non-cardiovascularstructures. In some embodiments, the non-cardiovascular structureparameters or non-CVD values of the one or more known datasets can bederived from medical images taken from other subjects, including healthysubjects and/or subjects with varying levels of risk. In someembodiments, the one or more known datasets of non-cardiovascularstructure parameters or non-CVD values can be stored in anon-cardiovascular structure values or non-CVD database 330 that can belocally accessible by the system and/or remotely accessible via anetwork connection by the system.

In some embodiments, at block 332, the system can be configured toupdate the risk of cardiovascular event for the subject based on thecomparison to the one or more known datasets of non-cardiovascularstructure parameters or non-CVD values. For example, based on thecomparison, the system may increase or decrease the previously generatedrisk assessment. In some embodiments, the system may maintain thepreviously generated risk assessment even after comparison.

In some embodiments, at block 334, the system can be configured togenerate a quantified color map, which can include color coding for oneor more non-cardiovascular structures identified from the medical image,as well as for the other cardiovascular structures identified from themedical image, stable plaque, unstable plaque, arteries, and/or thelike. In some embodiments, at block 336, the system can be configured togenerate a proposed treatment for the subject based on the generatedand/or updated risk assessment after comparison with the known datasetsof non-cardiovascular structure parameters or non-CVD values.

In some embodiments, one or more processes described herein inconnection with FIG. 3A can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for tracking of risk assessment of the subject basedon image processing and/or other purposes.

Quantification of Atherosclerosis

In some embodiments, the system is configured to analyze one or morearteries present in a medical image, such as CT scan data, toautomatically and/or dynamically quantify atherosclerosis. In someembodiments, the system is configured to quantify atherosclerosis as theprimary disease process, while stenosis and/or ischemia can beconsidered surrogates thereof. Prior to the embodiments describedherein, it was not feasible to quantify the primary disease due to thelengthy manual process and manpower needed to do so, which could takeanywhere from 4 to 8 or more hours. In contrast, in some embodiments,the system is configured to quantify atherosclerosis based on analysisof a medical image and/or CT scan using one or more AI, ML, and/or otheralgorithms that can segment, identify, and/or quantify atherosclerosisin less than about 1 minute, about 2 minutes, about 3 minutes, about 4minutes, about 5 minutes, about 6 minutes, about 7 minutes, about 8minutes, about 9 minutes, about 10 minutes, about 11 minutes, about 12minutes, about 13 minutes, about 14 minutes, about 15 minutes, about 20minutes, about 25 minutes, about 30 minutes, about 40 minutes, about 50minutes, and/or about 60 minutes. In some embodiments, the system isconfigured to quantify atherosclerosis within a time frame defined bytwo of the aforementioned values. In some embodiments, the system isconfigured to calculate stenosis rather than simply eyeballing, therebyallowing users to better understand whole heart atherosclerosis and/orguaranteeing the same calculated stenosis result if the same medicalimage is used for analysis. Importantly, the type of atherosclerosis canalso be quantified and/or classified by this method. Types ofatherosclerosis can be determined binarily (calcified vs. non-calcifiedplaque), ordinally (dense calcified plaque, calcified plaque, fibrousplaque, fibrofatty plaque, necrotic core, or admixtures of plaquetypes), or continuously (by attenuation density on a Hounsfield unitscale or similar).

FIG. 3B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for quantification and/or classification ofatherosclerosis based on medical image analysis. As illustrated in FIG.3B, in some embodiments, the system can be configured to access amedical image at block 202, such as a CT scan of a coronary region of asubject. Further, in some embodiments, the system can be configured toidentify one or more arteries at block 204 and/or one or more regions ofplaque at block 206. In addition, in some embodiments, the system can beconfigured to determine one or more vascular morphology and/orquantified plaque parameters at block 208. For example, in someembodiments, the system can be configured to determine a geometry and/orvolume of a region of plaque and/or a vessel at block 201, a ratio orfunction of volume to surface area of a region of plaque at block 203, aheterogeneity or homogeneity index of a region of plaque at block 205,radiodensity of a region of plaque and/or a composition thereof byranges of radiodensity values at block 207, a ratio of radiodensity tovolume of a region of plaque at block 209, and/or a diffusivity of aregion of plaque at block 211. Additional detail regarding the processesand techniques represented in blocks 202, 204, 206, 208, 201, 203, 205,207, 209, and 211 can be found in the description above in relation toFIG. 2A.

In some embodiments, the system can be configured quantify and/orclassify atherosclerosis at block 340 based on the determined one ormore vascular morphology and/or quantified plaque parameters. In someembodiments, the system can be configured to generate a weighted measureof one or more vascular morphology parameters and/or quantified plaqueparameters determined and/or derived from raw medical images. Forexample, in some embodiments, the system can be configured to weight oneor more vascular morphology parameters and/or quantified plaqueparameters equally. In some embodiments, the system can be configuredweight one or more vascular morphology parameters and/or quantifiedplaque parameters differently. In some embodiments, the system can beconfigured weight one or more vascular morphology parameters and/orquantified plaque parameters logarithmically, algebraically, and/orutilizing another mathematical transform. In some embodiments, thesystem is configured to quantify and/or classify atherosclerosis atblock 340 using the weighted measure and/or using only some of thevascular morphology parameters and/or quantified plaque parameters.

In some embodiments, the system is configured to generate a weightedmeasure of the one or more vascular morphology parameters and/orquantified plaque parameters by comparing the same to one or more knownvascular morphology parameters and/or quantified plaque parameters thatare derived from medical images of other subjects. For example, the oneor more known vascular morphology parameters and/or quantified plaqueparameters can be derived from one or more healthy subjects and/orsubjects at risk of coronary vascular disease.

In some embodiments, the system is configured to classifyatherosclerosis of a subject based on the quantified atherosclerosis asone or more of high risk, medium risk, or low risk. In some embodiments,the system is configured to classify atherosclerosis of a subject basedon the quantified atherosclerosis using an AI, ML, and/or otheralgorithm. In some embodiments, the system is configured to classifyatherosclerosis of a subject by combining and/or weighting one or moreof a ratio of volume of surface area, volume, heterogeneity index, andradiodensity of the one or more regions of plaque.

In some embodiments, a plaque having a low ratio of volume to surfacearea or a low absolute volume itself can indicate that the plaque isstable. As such, in some embodiments, the system can be configured todetermine that a ratio of volume to surface area of a region of plaquebelow a predetermined threshold is indicative of a low riskatherosclerosis. Thus, in some embodiments, the system can be configuredto take into account the number and/or sides of a plaque. For example,if there are a higher number of plaques with smaller sides, then thatcan be associated with a higher surface area or more irregularity, whichin turn can be associated with a higher surface area to volume ratio. Incontrast, if there are fewer number of plaques with larger sides or moreregularity, then that can be associated with a lower surface area tovolume ratio or a higher volume to surface area ratio. In someembodiments, a high radiodensity value can indicate that a plaque ishighly calcified or stable, whereas a low radiodensity value canindicate that a plaque is less calcified or unstable. As such, in someembodiments, the system can be configured to determine that aradiodensity of a region of plaque above a predetermined threshold isindicative of a low risk atherosclerosis. In some embodiments, a plaquehaving a low heterogeneity or high homogeneity can indicate that theplaque is stable. As such, in some embodiments, the system can beconfigured to determine that a heterogeneity of a region of plaque belowa predetermined threshold is indicative of a low risk atherosclerosis.

In some embodiments, at block 342, the system is configured to calculateor determine a numerical calculation or representation of coronarystenosis based on the quantified and/or classified atherosclerosisderived from the medical image. In some embodiments, the system isconfigured to calculate stenosis using the one or more vascularmorphology parameters and/or quantified plaque parameters derived fromthe medical image of a coronary region of the subject.

In some embodiments, at block 344, the system is configured to predict arisk of ischemia for the subject based on the quantified and/orclassified atherosclerosis derived from the medical image. In someembodiments, the system is configured to calculate a risk of ischemiausing the one or more vascular morphology parameters and/or quantifiedplaque parameters derived from the medical image of a coronary region ofthe subject.

In some embodiments, the system is configured to generate a proposedtreatment for the subject based on the quantified and/or classifiedatherosclerosis, stenosis, and/or risk of ischemia, wherein all of theforegoing are derived automatically and/or dynamically from a rawmedical image using image processing algorithms and techniques.

In some embodiments, one or more processes described herein inconnection with FIG. 3A can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for tracking of quantified atherosclerosis for asubject and/or other purposes.

Quantification of Plaque, Stenosis, and/or CAD-RADS Score

As discussed herein, in some embodiments, the system is configured totake the guesswork out of interpretation of medical images and providesubstantially exact and/or substantially accurate calculations orestimates of stenosis percentage, atherosclerosis, and/or CoronaryArtery Disease—Reporting and Data System (CAD-RADS) score as derivedfrom a medical image. As such, in some embodiments, the system canenhance the reads of the imagers by providing comprehensive quantitativeanalyses that can improve efficiency, accuracy, and/or reproducibility.

FIG. 3C is a flowchart illustrating an overview of an exampleembodiment(s) of a method for quantification of stenosis and generationof a CAD-RADS score based on medical image analysis. As illustrated inFIG. 3A, in some embodiments, the system can be configured to access amedical image at block 202. Additional detail regarding the types ofmedical images and other processes and techniques represented in block202 can be found in the description above in relation to FIG. 2A.

In some embodiments, at block 354, the system is configured to identifyone or more arteries, plaque, and/or fat in the medical image, forexample using AI, ML, and/or other algorithms. The processes andtechniques for identifying one or more arteries, plaque, and/or fat caninclude one or more of the same features as described above in relationto blocks 204 and 206. In particular, in some embodiments, the systemcan be configured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically identify one or more arteries,including for example coronary arteries, carotid arteries, aorta, renalartery, lower extremity artery, and/or cerebral artery. In someembodiments, one or more AI and/or ML algorithms can be trained using aConvolutional Neural Network (CNN) on a set of medical images on whicharteries have been identified, thereby allowing the AI and/or MLalgorithm automatically identify arteries directly from a medical image.In some embodiments, the arteries are identified by size and/orlocation.

Further, in some embodiments, the system can be configured to identifyone or more regions of plaque in the medical image, for example usingone or more AI and/or ML algorithms to automatically and/or dynamicallyidentify one or more regions of plaque. In some embodiments, the one ormore AI and/or ML algorithms can be trained using a Convolutional NeuralNetwork (CNN) on a set of medical images on which regions of plaque havebeen identified, thereby allowing the AI and/or ML algorithmautomatically identify regions of plaque directly from a medical image.In some embodiments, the system can be configured to identify a vesselwall and a lumen wall for each of the identified coronary arteries inthe medical image. In some embodiments, the system is then configured todetermine the volume in between the vessel wall and the lumen wall asplaque. In some embodiments, the system can be configured to identifyregions of plaque based on the radiodensity values typically associatedwith plaque, for example by setting a predetermined threshold or rangeof radiodensity values that are typically associated with plaque with orwithout normalizing using a normalization device.

Similarly, in some embodiments, the system can be configured to identifyone or more regions of fat, such as epicardial fat, in the medicalimage, for example using one or more AI and/or ML algorithms toautomatically and/or dynamically identify one or more regions of fat. Insome embodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich regions of fat have been identified, thereby allowing the AIand/or ML algorithm automatically identify regions of fat directly froma medical image. In some embodiments, the system can be configured toidentify regions of fat based on the radiodensity values typicallyassociated with fat, for example by setting a predetermined threshold orrange of radiodensity values that are typically associated with fat withor without normalizing using a normalization device.

In some embodiments, the system can be configured to determine one ormore vascular morphology and/or quantified plaque parameters at block208. For example, in some embodiments, the system can be configured todetermine a geometry and/or volume of a region of plaque and/or a vesselat block 201, a ratio or function of volume to surface area of a regionof plaque at block 203, a heterogeneity or homogeneity index of a regionof plaque at block 205, radiodensity of a region of plaque and/or acomposition thereof by ranges of radiodensity values at block 207, aratio of radiodensity to volume of a region of plaque at block 209,and/or a diffusivity of a region of plaque at block 211. Additionaldetail regarding the processes and techniques represented in blocks 208,201, 203, 205, 207, 209, and 211 can be found in the description abovein relation to FIG. 2A.

In some embodiments, at block 358, the system is configured to calculateor determine a numerical calculation or representation of coronarystenosis based on the one or more vascular morphology parameters and/orquantified plaque parameters derived from the medical image of acoronary region of the subject. In some embodiments, the system can beconfigured to generate a weighted measure of one or more vascularmorphology parameters and/or quantified plaque parameters determinedand/or derived from raw medical images. For example, in someembodiments, the system can be configured weight one or more vascularmorphology parameters and/or quantified plaque parameters equally. Insome embodiments, the system can be configured to weight one or morevascular morphology parameters and/or quantified plaque parametersdifferently. In some embodiments, the system can be configured weightone or more vascular morphology parameters and/or quantified plaqueparameters logarithmically, algebraically, and/or utilizing anothermathematical transform. In some embodiments, the system is configured tocalculate stenosis at block 358 using the weighted measure and/or usingonly some of the vascular morphology parameters and/or quantified plaqueparameters. In some embodiments, the system can be configured tocalculate stenosis on a vessel-by-vessel basis or a region-by-regionbasis.

In some embodiments, based on the calculated stenosis, the system isconfigured to determine a CAD-RADS score at block 360. This is incontrast to preexisting methods of determining a CAD-RADS based oneyeballing or general assessment of a medical image by a physician,which can result in unreproducible results. In some embodimentsdescribed herein, however, the system can be configured to generate areproducible and/or objective calculated CAD-RADS score based onautomatic and/or dynamic image processing of a raw medical image.

In some embodiments, at block 362, the system can be configured todetermine a presence or risk of ischemia based on the calculatedstenosis, one or more quantified plaque parameters and/or vascularmorphology parameters derived from the medical image. For example, insome embodiments, the system can be configured to determine a presenceor risk of ischemia by combining one or more of the foregoingparameters, either weighted or not, or by using some or all of theseparameters on an individual basis. In some embodiments, the system canbe configured to determine a presence of risk of ischemia by comparingone or more of the calculated stenosis, one or more quantified plaqueparameters and/or vascular morphology parameters to a database of knownsuch parameters derived from medical images of other subjects, includingfor example healthy subjects and/or subjects at risk of a cardiovascularevent. In some embodiments, the system can be configured to calculatepresence or risk of ischemia on a vessel-by-vessel basis or aregion-by-region basis.

In some embodiments, at block 364, the system can be configured todetermine one or more quantified parameters of fat for one or moreregions of fat identified from the medical image. For example, in someembodiments, the system can utilize any of the processes and/ortechniques discussed herein in relation to deriving quantifiedparameters of plaque, such as those described in connection with blocks208, 201, 203, 205, 207, 209, and 211. In particular, in someembodiments, the system can be configured to determine one or moreparameters of fat, including volume, geometry, radiodensity, and/or thelike of one or more regions of fat within the medical image.

In some embodiments, at block 366, the system can be configured togenerate a risk assessment of cardiovascular disease or event for thesubject. In some embodiments, the generated risk assessment can comprisea risk score indicating a risk of coronary disease for the subject. Insome embodiments, the system can generate a risk assessment based on ananalysis of one or more vascular morphology parameters, one or morequantified plaque parameters, one or more quantified fat parameters,calculated stenosis, risk of ischemia, CAD-RADS score, and/or the like.In some embodiments, the system can be configured to generate a weightedmeasure of one or more vascular morphology parameters, one or morequantified plaque parameters, one or more quantified fat parameters,calculated stenosis, risk of ischemia, and/or CAD-RADS score of thesubject. For example, in some embodiments, the system can be configuredweight one or more of the foregoing parameters equally. In someembodiments, the system can be configured weight one or more of theseparameters differently. In some embodiments, the system can beconfigured weight one or more of these parameters logarithmically,algebraically, and/or utilizing another mathematical transform. In someembodiments, the system is configured to generate a risk assessment ofcoronary disease or cardiovascular event for the subject at block 366using the weighted measure and/or using only some of these parameters.

In some embodiments, the system can be configured to generate a riskassessment of coronary disease or cardiovascular event for the subjectby combining one or more of the foregoing parameters, either weighted ornot, or by using some or all of these parameters on an individual basis.In some embodiments, the system can be configured to generate a riskassessment of coronary disease or cardiovascular event by comparing oneor more vascular morphology parameters, one or more quantified plaqueparameters, one or more quantified fat parameters, calculated stenosis,risk of ischemia, and/or CAD-RADS score of the subject to a database ofknown such parameters derived from medical images of other subjects,including for example healthy subjects and/or subjects at risk of acardiovascular event.

Further, in some embodiments, the system can be configured toautomatically and/or dynamically generate a CAD-RADS modifier based onone or more of the determined one or more vascular morphologyparameters, the set of quantified plaque parameters of the one or moreregions of plaque, the quantified coronary stenosis, the determinedpresence or risk of ischemia, and/or the determined set of quantifiedfat parameters. In particular, in some embodiments, the system can beconfigured to automatically and/or dynamically generate one or moreapplicable CAD-RADS modifiers for the subject, including for example oneor more of nondiagnostic (N), stent (S), graft (G), or vulnerability(V), as defined by and used by CAD-RADS. For example, N can indicatethat a study is non-diagnostic, S can indicate the presence of a stent,G can indicate the presence of a coronary artery bypass graft, and V canindicate the presence of vulnerable plaque, for example showing a lowradiodensity value.

In some embodiments, the system can be configured to generate a proposedtreatment for the subject based on the generated risk assessment ofcoronary disease, one or more vascular morphology parameters, one ormore quantified plaque parameters, one or more quantified fatparameters, calculated stenosis, risk of ischemia, CAD-RADS score,and/or CAD-RADS modifier derived from the raw medical image using imageprocessing.

In some embodiments, one or more processes described herein inconnection with FIG. 3B can be repeated. For example, if a medical imageof the same subject is taken again at a later point in time, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for tracking of quantified plaque, calculatedstenosis, CAD-RADS score and/or modifier derived from a medicalimage(s), risk determined risk of ischemia, quantified fat parameters,generated risk assessment of coronary disease for a subject, and/orother purposes.

Disease Tracking

In some embodiments, the systems, methods, and devices described hereincan be configured to track the progression and/or regression of anarterial and/or plaque-based disease, such as a coronary disease. Forexample, in some embodiments, the system can be configured to track theprogression and/or regression of a disease by automatically and/ordynamically analyzing a plurality of medical images obtained fromdifferent times using one or more techniques discussed herein andcomparing different parameters derived therefrom. As such, in someembodiments, the system can provide an automated disease tracking toolusing non-invasive raw medical images as an input, which does not relyon subjective assessment.

In particular, in some embodiments, the system can be configured toutilize a four-category system to determine whether plaque stabilizationor worsening is occurring in a subject. For example, in someembodiments, these categories can include: (1) “plaque progression” or“rapid plaque progression”; (2) “mixed response—calcium dominant” or“non-rapid calcium dominant mixed response”; (3) “mixedresponse—non-calcium dominant” or “non-rapid non-calcium dominant mixedresponse”; or (4) “plaque regression.”

In some embodiments, in “plaque progression” or “rapid plaqueprogression,” the overall volume or relative volume of plaque increases.In some embodiments, in “mixed response—calcium dominant” or “non-rapidcalcium dominant mixed response,” the plaque volume remains relativelyconstant or does not increase to the threshold level of “rapid plaqueprogression” but there is a general progression of calcified plaque anda general regression of non-calcified plaque. In some embodiments, in“mixed response—non-calcium dominant” or “non-rapid non-calcium dominantmixed response,” the plaque volume remains relatively constant but thereis a general progression of non-calcified plaque and a generalregression of calcified plaque. In some embodiments, in “plaqueregression,” the overall volume or relative volume of plaque decreases.

In some embodiments, these 4 categories can be expanded to be moregranular, for example including for higher vs. lower density calciumplaques (e.g., for those >vs.<1000 Hounsfield units) and/or tocategorize more specifically in calcium-dominant and non-calcifiedplaque-dominant mixed response. For example, for the non-calcifiedplaque-dominant mixed response, the non-calcified plaque can furtherinclude necrotic core, fibrofatty plaque and/or fibrous plaque asseparate categories within the overall umbrella of non-calcified plaque.Similarly, calcified plaques can be categorized as lower densitycalcified plaques, medium density calcified plaques and high densitycalcified plaques.

FIG. 3D is a flowchart illustrating an overview of an exampleembodiment(s) of a method for disease tracking based on medical imageanalysis. For example, in some embodiments, the system can be configuredto track the progression and/or regression of a plaque-based disease orcondition, such as a coronary disease relating to or involvingatherosclerosis, stenosis, ischemia, and/or the like, by analyzing oneor more medical images obtained non-invasively.

As illustrated in FIG. 3D, in some embodiments, the system at block 372is configured to access a first set of plaque parameters derived from amedical image of a subject at a first point in time. In someembodiments, the medical image can be stored in a medical image database100 and can include any of the types of medical images described above,including for example CT, non-contrast CT, contrast-enhanced CT, MR,DECT, Spectral CT, and/or the like. In some embodiments, the medicalimage of the subject can comprise the coronary region, coronaryarteries, carotid arteries, renal arteries, abdominal aorta, cerebralarteries, lower extremities, and/or upper extremities of the subject. Insome embodiments, the set of plaque parameters can be stored in a plaqueparameter database 370, which can include any of the quantified plaqueparameters discussed above in relation to blocks 208, 201, 203, 205,207, 209, and/or 211.

In some embodiments, the system can be configured to directly access thefirst set of plaque parameters that were previously derived from amedical image(s) and/or stored in a plaque parameter database 370. Insome embodiments, the plaque parameter database 370 can be locallyaccessible and/or remotely accessible by the system via a networkconnection. In some embodiments, the system can be configured todynamically and/or automatically derive the first set of plaqueparameters from a medical image taken from a first point in time.

In some embodiments, at block 374, the system can be configured toaccess a second medical image(s) of the subject, which can be obtainedfrom the subject at a later point in time than the medical image fromwhich the first set of plaque parameters were derived. In someembodiments, the medical image can be stored in a medical image database100 and can include any of the types of medical images described above,including for example CT, non-contrast CT, contrast-enhanced CT, MR,DECT, Spectral CT, and/or the like.

In some embodiments, at block 376, the system can be configured todynamically and/or automatically derive a second set of plaqueparameters from the second medical image taken from the second point intime. In some embodiments, the second set of plaque parameters caninclude any of the quantified plaque parameters discussed above inrelation to blocks 208, 201, 203, 205, 207, 209, and/or 211. In someembodiments, the system can be configured to store the derived ordetermined second set of plaque parameters in the plaque parameterdatabase 370.

In some embodiments, at block 378, the system can be configured toanalyze changes in one or more plaque parameters between the first setderived from a medical image taken at a first point in time to thesecond set derived from a medical image taken at a later point in time.For example, in some embodiments, the system can be configured tocompare a quantified plaque parameter between the two scans, such as forexample radiodensity, volume, geometry, location, ratio or function ofvolume to surface area, heterogeneity index, radiodensity composition,radiodensity composition as a function of volume, ratio of radiodensityto volume, diffusivity, any combinations or relations thereof, and/orthe like of one or more regions of plaque. In some embodiments, thesystem can be configured to determine the heterogeneity index of one ormore regions of plaque by generating a spatial mapping or athree-dimensional histogram of radiodensity values across a geometricshape of one or more regions of plaque. In some embodiments, the systemis configured to analyze changes in one or more non-image based metrics,such as for example serum biomarkers, genetics, omics, transcriptomics,microbiomics, and/or metabolomics.

In some embodiments, the system is configured to determine a change inplaque composition in terms of radiodensity or stable v. unstable plaquebetween the two scans. For example, in some embodiments, the system isconfigured to determine a change in percentage of higher radiodensity orstable plaques v. lower radiodensity or unstable plaques between the twoscans. In some embodiments, the system can be configured to track achange in higher radiodensity plaques v. lower radiodensity plaquesbetween the two scans. In some embodiments, the system can be configuredto define higher radiodensity plaques as those with a Hounsfield unit ofabove 1000 and lower radiodensity plaques as those with a Hounsfieldunit of below 1000.

In some embodiments, at block 380, the system can be configured todetermine the progression or regression of plaque and/or any otherrelated measurement, condition, assessment, or related disease based onthe comparison of the one or more parameters derived from two or morescans and/or change in one or more non-image based metrics, such asserum biomarkers, genetics, omics, transcriptomics, microbiomics, and/ormetabolomics. For example, in some embodiments, the system can beconfigured to determine the progression and/or regression of plaque ingeneral, atherosclerosis, stenosis, risk or presence of ischemia, and/orthe like. Further, in some embodiments, the system can be configured toautomatically and/or dynamically generate a CAD-RADS score of thesubject based on the quantified or calculated stenosis, as derived fromthe two medical images. Additional detail regarding generating aCAD-RADS score is described herein in relation to FIG. 3C. In someembodiments, the system can be configured to determine a progression orregression in the CAD-RADS score of the subject. In some embodiments,the system can be configured to compare the plaque parametersindividually and/or combining one or more of them as a weighted measure.For example, in some embodiments, the system can be configured to weightthe plaque parameters equally, differently, logarithmically,algebraically, and/or utilizing another mathematical transform. In someembodiments, the system can be configured to utilize only some or all ofthe quantified plaque parameters.

In some embodiments, the state of plaque progression as determined bythe system can include one of four categories, including rapid plaqueprogression, non-rapid calcium dominant mixed response, non-rapidnon-calcium dominant mixed response, or plaque regression. In someembodiments, the system is configured to classify the state of plaqueprogression as rapid plaque progression when a percent atheroma volumeincrease of the subject is more than 1% per year. In some embodiments,the system is configured to classify the state of plaque progression asnon-rapid calcium dominant mixed response when a percent atheroma volumeincrease of the subject is less than 1% per year and calcified plaquerepresents more than 50% of total new plaque formation. In someembodiments, the system is configured to classify the state of plaqueprogression as non-rapid non-calcium dominant mixed response when apercent atheroma volume increase of the subject is less than 1% per yearand non-calcified plaque represents more than 50% of total new plaqueformation. In some embodiments, the system is configured to classify thestate of plaque progression as plaque regression when a decrease intotal percent atheroma volume is present.

In some embodiments, at block 382, the system can be configured togenerate a proposed treatment plan for the subject. For example, in someembodiments, the system can be configured to generate a proposedtreatment plan for the subject based on the determined progression orregression of plaque and/or any other related measurement, condition,assessment, or related disease based on the comparison of the one ormore parameters derived from two or more scans.

In some embodiments, one or more processes described herein inconnection with FIG. 3D can be repeated. For example, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for continued tracking of a plaque-based diseaseand/or other purposes.

Determination of Cause of Change in Calcium Score

In some embodiments, the systems, methods and devices disclosed hereincan be configured to generate analysis and/or reports that can determinethe likely cause of an increased calcium score. A high or increasedcalcium score alone is not representative of any specific cause, eitherpositive or negative. Rather, in general, there can be various possiblecauses for a high or increased calcium score. For example, in somecases, a high or increased calcium score can be an indicator ofsignificant heart disease and/or that the patient is at increased riskof a heart attack. Also, in some cases, a high or increased calciumscore can be an indicator that the patient is increasing the amount ofexercise performed, because exercise can convert fatty material plaquewithin the artery vessel. In some cases, a high or increased calciumscore can be an indicator of the patient beginning a statin regimenwherein the statin is converting the fatty material plaque into calcium.Unfortunately, a blood test alone cannot be used to determine which ofthe foregoing reasons is the likely cause of an increased calcium score.In some embodiments, by utilizing one or more techniques describedherein, the system can be configured to determine the cause of anincreased or high calcium score.

More specifically, in some embodiments, the system can be configured totrack a particular segment of an artery wall vessel of a patient in sucha way to monitor the conversion of a fatty deposit material plaquelesion to a mostly calcified plaque deposit, which can be helpful indetermining the cause of an increase calcium score, such as one or moreof the causes identified above. In addition, in some embodiments, thesystem can be configured to determine and/or use the location, size,shape, diffusivity and/or the attenuation radiodensity of one or moreregions of calcified plaque to determine the cause of an increase incalcium score. As a non-limiting example, if a calcium plaque increasesin density, this may represent a stabilization of plaque by treatment orlifestyle, whereas if a new calcium plaque forms where one was not therebefore (particularly with a lower attenuation density), this mayrepresent an adverse finding of disease progression rather thanstabilization. In some embodiments, one or more processes and techniquesdescribed herein may be applied for non-contrast CT scans (such as anECG gated coronary artery calcium score or non-ECG gated chest CT) aswell as contrast-enhanced CT scans (such as a coronary CT angiogram).

As another non-limiting example, the CT scan image acquisitionparameters can be altered to improve understanding of calcium changesover time. As an example, traditional coronary artery calcium imaging isdone using a 2.5-3.0 mm slice thickness and detecting voxels/pixels thatare 130 Hounsfield units or greater. An alternative may be to do “thin”slice imaging with 0.5 mm slice thickness or similar; and detecting allHounsfield units densities below 130 and above a certain threshold(e.g., 100) that may identify less dense calcium that may be missed byan arbitrary 130 Hounsfield unit threshold.

FIG. 3E is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of cause of change incalcium score, whether an increase or decrease, based on medical imageanalysis.

As illustrated in FIG. 3E, in some embodiments, the system can beconfigured to access a first calcium score and/or a first set of plaqueparameters of a subject at block 384. The first calcium score and/or afirst set of plaque parameters can be derived from a medical image of asubject and/or from a blood test at a first point in time. In someembodiments, the medical image can be stored in a medical image database100 and can include any of the types of medical images described above,including for example CT, non-contrast CT, contrast-enhanced CT, MR,DECT, Spectral CT, and/or the like. In some embodiments, the medicalimage of the subject can comprise the coronary region, coronaryarteries, carotid arteries, renal arteries, abdominal aorta, cerebralarteries, lower extremities, and/or upper extremities of the subject. Insome embodiments, the set of plaque parameters can be stored in a plaqueparameter database 370, which can include any of the quantified plaqueparameters discussed above in relation to blocks 208, 201, 203, 205,207, 209, and/or 211.

In some embodiments, the system can be configured to directly accessand/or retrieve the first calcium score and/or first set of plaqueparameters that are stored in a calcium score database 398 and/or plaqueparameter database 370 respectively. In some embodiments, the plaqueparameter database 370 and/or calcium score database 298 can be locallyaccessible and/or remotely accessible by the system via a networkconnection. In some embodiments, the system can be configured todynamically and/or automatically derive the first set of plaqueparameters and/or calcium score from a medical image and/or blood testof the subject taken from a first point in time.

In some embodiments, at block 386, the system can be configured toaccess a second calcium score and/or second medical image(s) of thesubject, which can be obtained from the subject at a later point in timethan the first calcium score and/or medical image from which the firstset of plaque parameters were derived. For example, in some embodiments,the second calcium score can be derived from the second medical imageand/or a second blood test taken of the subject at a second point intime. In some embodiments, the second calcium score can be stored in thecalcium score database 398. In some embodiments, the medical image canbe stored in a medical image database 100 and can include any of thetypes of medical images described above, including for example CT,non-contrast CT, contrast-enhanced CT, MR, DECT, Spectral CT, and/or thelike.

In some embodiments, at block 388, the system can be configured tocompare the first calcium score to the second calcium score anddetermine a change in the calcium score. However, as discussed above,this alone typically does not provide insight as to the cause of thechange in calcium score, if any. In some embodiments, if there is nostatistically significant change in calcium score between the tworeadings, for example if any difference is below a predeterminedthreshold value, then the system can be configured to end the analysisof the change in calcium score. In some embodiments, if there is astatistically significant change in calcium score between the tworeadings, for example if the difference is above a predeterminedthreshold value, then the system can be configured to continue itsanalysis.

In particular, in some embodiments, at block 390, the system can beconfigured to dynamically and/or automatically derive a second set ofplaque parameters from the second medical image taken from the secondpoint in time. In some embodiments, the second set of plaque parameterscan include any of the quantified plaque parameters discussed above inrelation to blocks 208, 201, 203, 205, 207, 209, and/or 211. In someembodiments, the system can be configured to store the derived ordetermined second set of plaque parameters in the plaque parameterdatabase 370.

In some embodiments, at block 392, the system can be configured toanalyze changes in one or more plaque parameters between the first setderived from a medical image taken at a first point in time to thesecond set derived from a medical image taken at a later point in time.For example, in some embodiments, the system can be configured tocompare a quantified plaque parameter between the two scans, such as forexample radiodensity, volume, geometry, location, ratio or function ofvolume to surface area, heterogeneity index, radiodensity composition,radiodensity composition as a function of volume, ratio of radiodensityto volume, diffusivity, any combinations or relations thereof, and/orthe like of one or more regions of plaque and/or one or more regionssurrounding plaque. In some embodiments, the system can be configured todetermine the heterogeneity index of one or more regions of plaque bygenerating a spatial mapping or a three-dimensional histogram ofradiodensity values across a geometric shape of one or more regions ofplaque. In some embodiments, the system is configured to analyze changesin one or more non-image based metrics, such as for example serumbiomarkers, genetics, omics, transcriptomics, microbiomics, and/ormetabolomics.

In some embodiments, the system is configured to determine a change inplaque composition in terms of radiodensity or stable v. unstable plaquebetween the two scans. For example, in some embodiments, the system isconfigured to determine a change in percentage of higher radiodensity orstable plaques v. lower radiodensity or unstable plaques between the twoscans. In some embodiments, the system can be configured to track achange in higher radiodensity plaques v. lower radiodensity plaquesbetween the two scans. In some embodiments, the system can be configuredto define higher radiodensity plaques as those with a Hounsfield unit ofabove 1000 and lower radiodensity plaques as those with a Hounsfieldunit of below 1000.

In some embodiments, the system can be configured to compare the plaqueparameters individually and/or combining one or more of them as aweighted measure. For example, in some embodiments, the system can beconfigured to weight the plaque parameters equally, differently,logarithmically, algebraically, and/or utilizing another mathematicaltransform. In some embodiments, the system can be configured to utilizeonly some or all of the quantified plaque parameters.

In some embodiments, at block 394, the system can be configured tocharacterize the change in calcium score of the subject based on thecomparison of the one or more plaque parameters, whether individuallyand/or combined or weighted. In some embodiments, the system can beconfigured to characterize the change in calcium score as positive,neutral, or negative. For example, in some embodiments, if thecomparison of one or more plaque parameters reveals that plaque isstabilizing or showing high radiodensity values as a whole for thesubject without generation of any new plaque, then the system can reportthat the change in calcium score is positive. In contrast, if thecomparison of one or more plaque parameters reveals that plaque isdestabilizing as a whole for the subject, for example due to generationof new unstable regions of plaque with low radiodensity values, withoutgeneration of any new plaque, then the system can report that the changein calcium score is negative. In some embodiments, the system can beconfigured to utilize any or all techniques of plaque quantificationand/or tracking of plaque-based disease analysis discussed herein,include those discussed in connection with FIGS. 3A, 3B, 3C, and 3D.

As a non-limiting example, in some embodiments, the system can beconfigured to characterize the cause of a change in calcium score basedon determining and comparing a change in ratio between volume andradiodensity of one or more regions of plaque between the two scans.Similarly, in some embodiments, the system can be configured tocharacterize the cause of a change in calcium score based on determiningand comparing a change in diffusivity and/or radiodensity of one or moreregions of plaque between the two scans. For example, if theradiodensity of a region of plaque has increased, the system can beconfigured to characterize the change or increase in calcium score aspositive. In some embodiments, if the system identifies one or more newregions of plaque in the second image that were not present in the firstimage, the system can be configured to characterize the change incalcium score as negative. In some embodiments, if the system determinesthat the volume to surface area ratio of one or more regions of plaquehas decreased between the two scans, the system can be configured tocharacterize the change in calcium score as positive. In someembodiments, if the system determines that a heterogeneity orheterogeneity index of a region is plaque has decreased between the twoscans, for example by generating and/or analyzing spatial mapping ofradiodensity values, then the system can be configured to characterizethe change in calcium score as positive.

In some embodiments, the system is configured to utilize an AI, ML,and/or other algorithm to characterize the change in calcium score basedon one or more plaque parameters derived from a medical image. Forexample, in some embodiments, the system can be configured to utilize anAI and/or ML algorithm that is trained using a CNN and/or using adataset of known medical images with identified plaque parameterscombined with calcium scores. In some embodiments, the system can beconfigured to characterize a change in calcium score by accessing knowndatasets of the same stored in a database. For example, the knowndataset may include datasets of changes in calcium scores and/or medicalimages and/or plaque parameters derived therefrom of other subjects inthe past. In some embodiments, the system can be configured tocharacterize a change in calcium score and/or determine a cause thereofon a vessel-by-vessel basis, segment-by-segment basis, plaque-by-plaquebasis, and/or a subject basis.

In some embodiments, at block 396, the system can be configured togenerate a proposed treatment plan for the subject. For example, in someembodiments, the system can be configured to generate a proposedtreatment plan for the subject based on the change in calcium scoreand/or characterization thereof for the subject.

In some embodiments, one or more processes described herein inconnection with FIG. 3E can be repeated. For example, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for continued tracking and/or characterization ofchanges in calcium score for a subject and/or other purposes.

Prognosis of Cardiovascular Event

In some embodiments, the systems, devices, and methods described hereinare configured to generate a prognosis of a cardiovascular event for asubject based on one or more of the medical image-based analysistechniques described herein. For example, in some embodiments, thesystem is configured to determine whether a patient is at risk for acardiovascular event based on the amount of bad plaque buildup in thepatient's artery vessels. For this purpose, a cardiovascular event caninclude clinical major cardiovascular events, such as heart attack,stroke or death, as well as disease progression and/or ischemia.

In some embodiments, the system can identify the risk of acardiovascular event based on a ratio of the amount and/or volume of badplaque buildup versus the total surface area and/or volume of some orall of the artery vessels in a patient. In some embodiments, if theforegoing ratio exceeds a certain threshold, the system can beconfigured to output a certain risk factor and/or number and/or levelassociated with the patient. In some embodiments, the system isconfigured to determine whether a patient is at risk for acardiovascular event based on an absolute amount or volume or a ratio ofthe amount or volume bad plaque buildup in the patient's artery vesselscompared to the total volume of some or all of the artery vessels. Insome embodiments, the system is configured to determine whether apatient is at risk for a cardiovascular event based on results fromblood chemistry or biomarker tests of the patient, for example whethercertain blood chemistry or biomarker tests of the patient exceed certainthreshold levels. In some embodiments, the system is configured toreceive as input from the user or other systems and/or access bloodchemistry or biomarker tests data of the patient from a database system.In some embodiments, the system can be configured to utilize not onlyartery information related to plaque, vessel morphology, and/or stenosisbut also input from other imaging data about the non-coronarycardiovascular system, such as subtended left ventricular mass, chambervolumes and size, valvular morphology, vessel (e.g., aorta, pulmonaryartery) morphology, fat, and/or lung and/or bone health. In someembodiments, the system can utilize the outputted risk factor togenerate a treatment plan proposal. For example, the system can beconfigured to output a treatment plan that involves the administrationof cholesterol reducing drugs, such as statins, in order to transformthe soft bad plaque into hard plaque that is safer and more stable for apatient. In general, hard plaque that is largely calcified can have asignificant lower risk of rupturing into the artery vessel therebydecreasing the chances of a clot forming in the artery vessel which candecrease a patient's risk of a heart attack or other cardiac event.

FIG. 4A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for prognosis of a cardiovascular event basedon and/or derived from medical image analysis.

As illustrated in FIG. 4A, in some embodiments, the system can beconfigured to access a medical image at block 202, such as a CT scan ofa coronary region of a subject, which can be stored in a medical imagedatabase 100. Further, in some embodiments, the system can be configuredto identify one or more arteries at block 204 and/or one or more regionsof plaque at block 206. In addition, in some embodiments, the system canbe configured to determine one or more vascular morphology and/orquantified plaque parameters at block 208. For example, in someembodiments, the system can be configured to determine a geometry and/orvolume of a region of plaque and/or a vessel, a ratio or function ofvolume to surface area of a region of plaque, a heterogeneity orhomogeneity index of a region of plaque, radiodensity of a region ofplaque and/or a composition thereof by ranges of radiodensity values, aratio of radiodensity to volume of a region of plaque, and/or adiffusivity of a region of plaque. In addition, in some embodiments, atblock 210, the system can be configured to classify one or more regionsof plaque as stable v. unstable or good v. bad based on the one or morevascular morphology parameters and/or quantified plaque parametersdetermined and/or derived from raw medical images. Additional detailregarding the processes and techniques represented in blocks 202, 204,206, 208, and 210 can be found in the description above in relation toFIG. 2A.

In some embodiments, the system at block 412 is configured to generate aratio of bad plaque to the vessel on which the bad plaque appears. Morespecifically, in some embodiments, the system can be configured todetermine a total surface area of a vessel identified on a medical imageand a surface area of all regions of bad or unstable plaque within thatvessel. Based on the foregoing, in some embodiments, the system can beconfigured to generate a ratio of surface area of all bad plaque withina particular vessel to the surface area of the entire vessel or aportion thereof shown in a medical image. Similarly, in someembodiments, the system can be configured to determine a total volume ofa vessel identified on a medical image and a volume of all regions ofbad or unstable plaque within that vessel. Based on the foregoing, insome embodiments, the system can be configured to generate a ratio ofvolume of all bad plaque within a particular vessel to the volume of theentire vessel or a portion thereof shown in a medical image.

In some embodiments, at block 414, the system is further configured todetermine a total absolute volume and/or surface area of all bad orunstable plaque identified in a medical image. Also, in someembodiments, at block 416, the system is configured to determine a totalabsolute volume of all plaque, including good plaque and bad plaque,identified in a medical image. Further, in some embodiments, at block418, the system can be configured to access or retrieve results from ablood chemistry and/or biomarker test of the patient and/or othernon-imaging test results. Furthermore, in some embodiments, at block422, the system can be configured to access and/or analyze one or morenon-coronary cardiovascular system medical images.

In some embodiments, at block 420, the system can be configured toanalyze one or more of the generated ratio of bad plaque to a vessel,whether by surface area or volume, total absolute volume of bad plaque,total absolute volume of plaque, blood chemistry and/or biomarker testresults, and/or analysis results of one or more non-coronarycardiovascular system medical images to determine whether one or more ofthese parameters, either individually and/or combined, is above apredetermined threshold. For example, in some embodiments, the systemcan be configured to analyze one or more of the foregoing parametersindividually by comparing them to one or more reference values ofhealthy subjects and/or subjects at risk of a cardiovascular event. Insome embodiments, the system can be configured to analyze a combination,such as a weighted measure, of one or more of the foregoing parametersby comparing the combined or weighted measure thereof to one or morereference values of healthy subjects and/or subjects at risk of acardiovascular event. In some embodiments, the system can be configuredto weight one or more of these parameters equally. In some embodiments,the system can be configured to weight one or more of these parametersdifferently. In some embodiments, the system can be configured to weightone or more of these parameters logarithmically, algebraically, and/orutilizing another mathematical transform. In some embodiments, thesystem can be configured to utilize only some of the aforementionedparameters, either individually, combined, and/or as part of a weightedmeasure.

In some embodiments, at block 424, the system is configured to generatea prognosis for a cardiovascular event for the subject. In particular,in some embodiments, the system is configured to generate a prognosisfor cardiovascular event based on one or more of the analysis results ofthe generated ratio of bad plaque to a vessel, whether by surface areaor volume, total absolute volume of bad plaque, total absolute volume ofplaque, blood chemistry and/or biomarker test results, and/or analysisresults of one or more non-coronary cardiovascular system medicalimages. In some embodiments, the system is configured to generate theprognosis utilizing an AI, ML, and/or other algorithm. In someembodiments, the generated prognosis comprises a risk score or riskassessment of a cardiovascular event for the subject. In someembodiments, the cardiovascular event can include one or more ofatherosclerosis, stenosis, ischemia, heart attack, and/or the like.

In some embodiments, at block 426, the system can be configured togenerate a proposed treatment plan for the subject. For example, in someembodiments, the system can be configured to generate a proposedtreatment plan for the subject based on the change in calcium scoreand/or characterization thereof for the subject. In some embodiments,the generated treatment plan can include use of statins, lifestylechanges, and/or surgery.

In some embodiments, one or more processes described herein inconnection with FIG. 4A can be repeated. For example, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used for continued prognosis of a cardiovascular eventfor a subject and/or other purposes.

Patient-Specific Stent Determination

In some embodiments, the systems, methods, and devices described hereincan be used to determine and/or generate one or more parameters for apatient-specific stent and/or selection or guidance for implantationthereof. In particular, in some embodiments, the systems disclosedherein can be used to dynamically and automatically determine thenecessary stent type, length, diameter, gauge, strength, and/or anyother stent parameter for a particular patient based on processing ofthe medical image data, for example using AI, ML, and/or otheralgorithms.

In some embodiments, by determining one or more patient-specific stentparameters that are best suited for a particular artery area, the systemcan reduce the risk of patient complications and/or insurance risksbecause if too large of a stent is implanted, then the artery wall canbe stretched too thin resulting in a possible rupture, or undesirablehigh flow, or other issues. On the other hand, if too small of a stentis implanted, then the artery wall might not be stretched open enoughresulting in too little blood flow or other issues.

In some embodiments, the system is configured to dynamically identify anarea of stenosis within an artery, dynamically determine a properdiameter of the identified area of the artery, and/or automaticallyselect a stent from a plurality of available stent options. In someembodiments, the selected stent can be configured to prop open theartery area after implantation to the determined proper artery diameter.In some embodiments, the proper artery diameter is determined to beequivalent or substantially equivalent to what the diameter wouldnaturally be without stenosis. In some embodiments, the system can beconfigured to dynamically generate a patient-specific surgical plan forimplanting the selected stent in the identified artery area. Forexample, the system can be configured to determine whether a bifurcationof the artery is near the identified artery area and generate apatient-specific surgical plan for inserting two guidewires for handlingthe bifurcation and/or determining the position for jailing andinserting a second stent into the bifurcation.

FIG. 4B is a flowchart illustrating an overview of an exampleembodiment(s) of a method for determination of patient-specific stentparameters based on medical image analysis.

As illustrated in FIG. 4B, in some embodiments, the system can beconfigured to access a medical image at block 202, such as a CT scan ofa coronary region of a subject. Further, in some embodiments, the systemcan be configured to identify one or more arteries at block 204 and/orone or more regions of plaque at block 206. In addition, in someembodiments, the system can be configured to determine one or morevascular morphology and/or quantified plaque parameters at block 208.For example, in some embodiments, the system can be configured todetermine a geometry and/or volume of a region of plaque and/or a vesselat block 201, a ratio or function of volume to surface area of a regionof plaque at block 203, a heterogeneity or homogeneity index of a regionof plaque at block 205, radiodensity of a region of plaque and/or acomposition thereof by ranges of radiodensity values at block 207, aratio of radiodensity to volume of a region of plaque at block 209,and/or a diffusivity of a region of plaque at block 211. Additionaldetail regarding the processes and techniques represented in blocks 202,204, 206, 208, 201, 203, 205, 207, 209, and 211 can be found in thedescription above in relation to FIG. 2A.

In some embodiments, at block 440, the system can be configured toanalyze the medical image to determine one or more vessel parameters,such as the diameter, curvature, vascular morphology, vessel wall, lumenwall, and/or the like. In some embodiments, the system can be configuredto determine or derive from the medical image one or more vesselparameters as shown in the medical image, for example with stenosis atcertain regions along the vessel. In some embodiments, the system can beconfigured to determine one or more vessel parameters without stenosis.For example, in some embodiments, the system can be configured tographically and/or hypothetically remove stenosis or plaque from avessel to determine the diameter, curvature, and/or the like of thevessel if stenosis did not exist.

In some embodiments, at block 442, the system can be configured todetermine whether a stent is recommended for the subject and, if so, oneor more recommended parameters of a stent specific for that patientbased on the medical analysis. For example, in some embodiments, thesystem can be configured to analyze one or more of the identifiedvascular morphology parameters, quantified plaque parameters, and/orvessel parameters. In some embodiments, the system can be configured toutilize an AI, ML, and/or other algorithm. In some embodiments, thesystem is configured to analyze one or more of the aforementionedparameters individually, combined, and/or as a weighted measure. In someembodiments, one or more of these parameters derived from a medicalimage, either individually or combined, can be compared to one or morereference values derived or collected from other subjects, includingthose who had a stent implanted and those who did not. In someembodiments, based on the determined parameters of a patient-specificstent, the system can be configured to determine a selection of apreexisting stent that matches those parameters and/or generatemanufacturing instructions to manufacture a patient-specific stent withstent parameters derived from a medical image. In some embodiments, thesystem can be configured to recommend a diameter of a stent that is lessthan or substantially equal to the diameter of an artery if stenosis didnot exist.

In some embodiments, at block 444, the system can be configured togenerate a recommended surgical plan for stent implantation based on theanalyzed medical image. For example, in some embodiments, the system canbe configured to determine whether a bifurcation exists based on themedical image and/or generate guidelines for the positioning ofguidewires and/or stent for the patient prior to surgery. As such, insome embodiments, the system can be configured to generate a detailedsurgical plan that is specific to a particular patient based on medicalimage analysis of plaque and/or other parameters.

In some embodiments, at block 446, the system is configured to access orretrieve one or more medical images after stent implantation. In someembodiments, at block 448, the system can be configured to analyze theaccessed medical image to perform post-implantation analysis. Forexample, in some embodiments, the system can be configured to derive oneor more vascular morphology and/or plaque parameters, including any ofthose discussed herein in relation to block 208, after stentimplantation. Based on analysis of the foregoing, in some embodiments,the system can generate further proposed treatment in some embodiments,such as for example recommended use of statins or other medications,lifestyle change, further surgery or stent implantation, and/or thelike.

In some embodiments, one or more processes described herein inconnection with FIG. 4B can be repeated. For example, one or moreprocesses described herein can be repeated and the analytical resultsthereof can be used to determine the need for and/or parameters of anadditional patient-specific stent for a patient and/or other purposes.

Patient-Specific Report

In some embodiments, the system is configured to dynamically generate apatient-specific report based on the analysis of the processed datagenerated from the raw CT scan data. In some embodiments, the patientspecific report is dynamically generated based on the processed data. Insome embodiments, the written report is dynamically generated based onselecting and/or combining certain phrases from a database, whereincertain words, terms, and/or phrases are altered to be specific to thepatient and the identified medical issues of the patient. In someembodiments, the system is configured to dynamically select one or moreimages from the image scanning data and/or the system generated imageviews described herein, wherein the selected one or more images aredynamically inserted into the written report in order to generate apatient-specific report based on the analysis of the processed data.

In some embodiments, the system is configured to dynamically annotatethe selected one or more images for insertion into the patient specificreport, wherein the annotations are specific to patient and/or areannotations based on the data processing performed by the devices,methods, and systems disclosed herein, for example, annotating the oneor more images to include markings or other indicators to show wherealong the artery there exists bad plaque buildup that is significant.

In some embodiments, the system is configured to dynamically generate areport based on past and/or present medical data. For example, in someembodiments, the system can be configured to show how a patient'scardiovascular health has changed over a period. In some embodiments,the system is configured to dynamically generate phrases and/or selectphrases from a database to specifically describe the cardiovascularhealth of the patient and/or how the cardiovascular disease has changedwithin a patient.

In some embodiments, the system is configured to dynamically select oneor more medical images from prior medical scanning and/or currentmedical scanning for insertion into the medical report in order to showhow the cardiovascular disease has changed over time in a patient, forexample, showing past and present images juxtaposed to each other, orfor example, showing past images that are superimposed on present imagesthereby allowing a user to move or fade or toggle between past andpresent images.

In some embodiments, the patient-specific report is an interactivereport that allows a user to interact with certain images, videos,animations, augmented reality (AR), virtual reality (VR), and/orfeatures of the report. In some embodiments, the system is configured toinsert into the patient-specific report dynamically generatedillustrations or images of patient artery vessels in order to highlightspecific vessels and/or portions of vessels that contain or are likelyto contain vascular disease that require review or further analysis. Insome embodiments, the dynamically generated patient-specific report isconfigured to show a user the vessel walls using AR and/or VR.

In some embodiments, the system is configured to insert into thedynamically generated report any ratios and/or dynamically generateddata using the methods, systems, and devices disclosed herein. In someembodiments, the dynamically generated report comprises a radiologyreport. In some embodiments, the dynamically generated report is in aneditable document, such as Microsoft Word®, in order to allow thephysician to make edits to the report. In some embodiments, thedynamically generated report is saved into a PACS (Picture Archiving andCommunication System) or other EMR (electronic medical records) system.

In some embodiments, the system is configured to transform and/ortranslate data from the imaging into drawings or infographics in a videoformat, with or without audio, in order to transmit accurately theinformation in a way that is better understandable to any patient toimprove literacy. In some embodiments, this method of improving literacyis coupled to a risk stratification tool that defines a lower risk withhigher literacy, and a higher risk with lower literacy. In someembodiments, these report outputs may be patient-derived and/orpatient-specific. In some embodiments, real patient imaging data (forexample, from their CT) can be coupled to graphics from their CT and/ordrawings from the CT to explain the findings further. In someembodiments, real patient imaging data, graphics data and/or drawingsdata can be coupled to an explaining graphic that is not from thepatient but that can help the patient better understand (for example, avideo about lipid-rich plaque).

In some embodiments, these patient reports can be imported into anapplication that allows for following disease over time in relation tocontrol of heart disease risk factors, such as diabetes or hypertension.In some embodiments, an app and/or user interface can allow forfollowing of blood glucose and blood pressure over time and/or relatethe changes of the image over time in a way that augments riskprediction.

In some embodiments, the system can be configured to generate a videoreport that is specific to the patient based on the processed datagenerated from the raw CT data. In some embodiments, the system isconfigured to generate and/or provide a personalized cinematic viewingexperience for a user, which can be programmed to automatically anddynamically change content based upon imaging findings, associatedauto-calculated diagnoses, and/or prognosis algorithms. In someembodiments, the method of viewing, unlike traditional reporting, isthrough a movie experience which can be in the form of a regular 2Dmovie and/or through a mixed reality movie experience through AR or VR.In some embodiments, in the case of both 2D and mixed reality, thepersonalized cinematic experience can be interactive with the patient topredict their prognosis, such as risk of heart attack, rate of diseaseprogression, and/or ischemia.

In some embodiments, the system can be configured to dynamicallygenerate a video report that comprises both cartoon images and/oranimation along with audio content in combination with actual CT imagedata from the patient. In some embodiments, the dynamically generatedvideo medical report is dynamically narrated based on selecting phrases,terms and/or other content from a database such that a voice synthesizeror pre-made voice content can be used for playback during the videoreport. In some embodiments, the dynamically generated video medicalreport is configured to comprise any of the images disclosed herein. Insome embodiments, the dynamically generated video medical report can beconfigured to dynamically select one or more medical images from priormedical scanning and/or current medical scanning for insertion into thevideo medical report in order to show how the cardiovascular disease haschanged over time in a patient. For example, in some embodiments, thereport can show past and present images juxtaposed next to each other.In some embodiments, the repot can show past images that aresuperimposed on present images thereby allowing a user to toggle or moveor fade between past and present images. In some embodiments, thedynamically generated video medical report can be configured to showactual medical images, such as a CT medical image, in the video reportand then transition to an illustrative view or cartoon view (partial orentirely an illustrative or cartoon view) of the actual medical images,thereby highlighting certain features of the patient's arteries. In someembodiments, the dynamically generated video medical report isconfigured to show a user the vessel walls using AR and/or VR.

FIG. 5A is a flowchart illustrating an overview of an exampleembodiment(s) of a method for generation of a patient-specific medicalreport based on medical image analysis. As illustrated in FIG. 5A, insome embodiments, the system can be configured to access a medical imageat block 202. In some embodiments, the medical image can be stored in amedical image database 100. Additional detail regarding the types ofmedical images and other processes and techniques represented in block202 can be found in the description above in relation to FIG. 2A.

In some embodiments, at block 354, the system is configured to identifyone or more arteries, plaque, and/or fat in the medical image, forexample using AI, ML, and/or other algorithms. Additional detailregarding the types of medical images and other processes and techniquesrepresented in block 354 can be found in the description above inrelation to FIG. 3C.

In some embodiments, at block 208, the system can be configured todetermine one or more vascular morphology and/or quantified plaqueparameters. For example, in some embodiments, the system can beconfigured to determine a geometry and/or volume of a region of plaqueand/or a vessel at block 201, a ratio or function of volume to surfacearea of a region of plaque at block 203, a heterogeneity or homogeneityindex of a region of plaque at block 205, radiodensity of a region ofplaque and/or a composition thereof by ranges of radiodensity values atblock 207, a ratio of radiodensity to volume of a region of plaque atblock 209, and/or a diffusivity of a region of plaque at block 211.Additional detail regarding the processes and techniques represented inblocks 208, 201, 203, 205, 207, 209, and 211 can be found in thedescription above in relation to FIG. 2A.

In some embodiments, at block 508, the system can be configured todetermine and/or quantify stenosis, atherosclerosis, risk of ischemia,risk of cardiovascular event or disease, and/or the like. The system canbe configured to utilize any techniques and/or algorithms describedherein, including but not limited to those described above in connectionwith block 358 and block 366 of FIG. 3C.

In some embodiments, at block 510, the system can be configured togenerate an annotated medical image and/or quantized color map using theanalysis results derived from the medical image. For example, in someembodiments, the system can be configured to generate a quantized mapshowing one or more arteries, plaque, fat, good plaque, bad plaque,vascular morphologies, and/or the like.

In some embodiments, at block 512, the system can be configured todetermine a progression of plaque and/or disease of the patient, forexample based on analysis of previously obtained medical images of thesubject. In some embodiments, the system can be configured to utilizeany algorithms or techniques described herein in relation to diseasetracking, including but not limited to those described in connectionwith block 380 and/or FIG. 3D generally.

In some embodiments, at block 514, the system can be configured togenerate a proposed treatment plan for the patient based on thedetermined progression of plaque and/or disease. In some embodiments,the system can be configured to utilize any algorithms or techniquesdescribed herein in relation to disease tracking and treatmentgeneration, including but not limited to those described in connectionwith block 382 and/or FIG. 3D generally.

In some embodiments, at block 516, the system can be configured togenerate a patient-specific report. The patient-specific report caninclude one or more medical images of the patient and/or derivedgraphics thereof. For example, in some embodiments, the patient reportcan include one or more annotated medical images and/or quantized colormaps. In some embodiments, the patient-specific report can include oneor more vascular morphology and/or quantified plaque parameters derivedfrom the medical image. In some embodiments, the patient-specific reportcan include quantified stenosis, atherosclerosis, ischemia, risk ofcardiovascular event or disease, CAD-RADS score, and/or progression ortracking of any of the foregoing. In some embodiments, thepatient-specific report can include a proposed treatment, such asstatins, lifestyle changes, and/or surgery.

In some embodiments, the system can be configured to access and/orretrieve from a patient report database 500 one or more phrases,characterizations, graphics, videos, audio files, and/or the like thatare applicable and/or can be used to generate the patient-specificreport. In generating the patient-specific report, in some embodiments,the system can be configured to compare one or more parameters, such asthose mentioned above and/or derived from a medical image of thepatient, with one or more parameters previously derived from otherpatients. For example, in some embodiments, the system can be configuredto compare one or more quantified plaque parameters derived from themedical image of the patient with one or more quantified plaqueparameters derived from medical images of other patients in the similaror same age group. Based on the comparison, in some embodiments, thesystem can be configured to determine which phrases, characterizations,graphics, videos, audio files, and/or the like to include in thepatient-specific report, for example by identifying similar previouscases. In some embodiments, the system can be configured to utilize anAI and/or ML algorithm to generate the patient-specific report. In someembodiments, the patient-specific report can include a document, ARexperience, VR experience, video, and/or audio component.

FIGS. 5B-5I illustrate example embodiment(s) of a patient-specificmedical report generated based on medical image analysis. In particular,FIG. 5B illustrates an example cover page of a patient-specific report.

FIGS. 5C-5I illustrate portions of an example patient-specificreport(s). In some embodiments, a patient-specific report generated bythe system may include only some or all of these illustrated portions.As illustrated in FIGS. 5C-5I, in some embodiments, the patient-specificreport includes a visualization of one or more arteries and/or portionsthereof, such as for example, the Right Coronary Artery (RCA),R-Posterior Descending Artery (R-PDA), R-Posterolateral Branch (R-PLB),Left Main (LM) and Left Anterior Descending (LAD) Artery, 1st Diagonal(D1) Artery, 2nd Diagonal (D2) Artery, Circumflex (Cx) Artery, 1stObtuse Marginal Branch (OM1), 2nd Obtuse Marginal Branch (OM2), RamusIntermedius (RI), and/or the like. In some embodiments, for each of thearteries included in the report, the system is configured to generate astraightened view for easy tracking along the length of the vessel, suchas for example at the proximal, mid, and/or distal portions of anartery.

In some embodiments, a patient-specific report generated by the systemincludes a quantified measure of various plaque and/or vascularmorphology-related parameters shown within the vessel. In someembodiments, for each or some of the arteries included in the report,the system is configured to generate and/or derive from a medical imageof the patient and include in a patient-specific report a quantifiedmeasure of the total plaque volume, total low-density or non-calcifiedplaque volume, total non-calcified plaque value, and/or total calcifiedplaque volume. Further, in some embodiments, for each or some of thearteries included in the report, the system is configured to generateand/or derive from a medical image of the patient and include in apatient-specific report a quantified measure of stenosis severity, suchas for example a percentage of the greatest diameter stenosis within theartery. In some embodiments, for each or some of the arteries includedin the patient-specific report, the system is configured to generateand/or derive from a medical image of the patient and include in apatient-specific report a quantified measure of vascular remodeling,such as for example the highest remodeling index.

Visualization/GUI

Atherosclerosis, the buildup of fats, cholesterol and other substancesin and on your artery walls (e.g., plaque), which can restrict bloodflow. The plaque can burst, triggering a blood clot. Althoughatherosclerosis is often considered a heart problem, it can affectarteries anywhere in the body. However, determining information aboutplaque in coronary arteries can be difficult due in part to imperfectimaging data, aberrations that can be present in coronary artery images(e.g., due to movement of the patient), and differences in themanifestation of plaque in different patients. Accordingly, neithercalculated information derived from CT images, or visual inspection ofthe CT images, alone provide sufficient information to determineconditions that exist in the patient's coronary arteries. Portions ofthis disclosure describe information they can be determined from CTimages using automatic or semiautomatic processes. For example, using amachine learning process has been trained on thousands of CT scansdetermine information depicted in the CT images, and/or utilizinganalyst to review and enhance the results of the machine learningprocess, and the example user interfaces described herein can providethe determined information to another analyst or a medical practitioner.While the information determined from the CT images is invaluable inassessing the condition of a patient's coronary arteries, visualanalysis of the coronary arteries by skilled medical practitioner, withthe information determined from the CT images in-hand, allows a morecomprehensive assessment of the patient's coronary arteries. Asindicated herein, embodiments of the system facilitate the analysis andvisualization of vessel lumens, vessel walls, plaque and stenosis in andaround coronary vessels. This system can display vessels in multi-planarformats, cross-sectional views, 3D coronary artery tree view, axial,sagittal, and coronal views based on a set of computerized tomography(CT) images, e.g., generated by a CT scan of a patient's vessels. The CTimages can be Digital Imaging and Communications in Medicine (DICOM)images, a standard for the communication and management of medicalimaging information and related data. CT images, or CT scans, as usedherein, is a broad term that refers to pictures of structures within thebody created by computer controlled scanner. For example, by a scannerthat uses an X-ray beam. However, it is appreciated that other radiationsources and/or imaging systems may produce a set of CT-like images.Accordingly, the use of the term “CT images” herein may refer to anytype of imaging system having any type of imaging source that produces aset of images depicting “slices” of structures within a body, unlessotherwise indicated. One key aspect of the user interface describedherein is the precise correlation of the views and information that isdisplayed of the CT images. Locations in the CT images displayed onportions (or “panels”) of the user interface are correlated precisely bythe system such that the same locations are displayed concurrently in adifferent views. By simultaneously displaying a portion of the coronaryvessel in, for example, two, three, four, five or six viewssimultaneously, and allowing a practitioner to explore particularlocations of a coronary vessel in one view while the other 2-6 viewscorrespondingly show the exact same location provides an enormous amountof insight into the condition of the vessel and allows thepractitioner/analyst to quickly and easily visually integrate thepresented information to gain a comprehensive and accurate understandingof the condition of the coronary vessel being examined.

Advantageously, the present disclosure allows CT images and data to beanalyzed in a more useful and accurate way, for users to interact andanalyze images and data in a more analytically useful way and/or forcomputation analysis to be performed in a more useful way, for exampleto detect conditions requiring attention. The graphical user interfacesin the processing described herein allow a user to visualize otherwisedifficult to define relationships between different information andviews of coronary arteries. In an example, displaying a portion of acoronary artery simultaneously in a CMPR view, a SMPR view, and across-sectional view can provide insight to an analyst of plaque orstenosis associated with the coronary artery that may not otherwise beperceivable using a fewer number of views. Similarly, displaying theportion of the coronary artery in an axial view, a sagittal view, and acoronal view, in addition to the CMPR view, the SMPR view, and thecross-sectional view can provide further information to the analyst thatwould not otherwise be perceivable with a fewer number of views of thecoronary artery. In various embodiments, any of the informationdescribed or illustrated herein, determined by the system or an analystinteracting with the system, and other information (for example, fromanother outside source, e.g., an analyst) that relates to coronaryarteries/vessels associated with the set of CT images (“arteryinformation”) including information indicative of stenosis and plaque ofsegments of the coronary vessels in the set of CT images, andinformation indicative of identification and location of the coronaryvessels in the set of CT images, can be stored on the system andpresented in various panels of the user interface and in reports. Thepresent disclosure allows for easier and quicker analysis of a patient'scoronary arteries and features associate with coronary arteries. Thepresent disclosure also allows faster analysis of coronary artery databy allowing quick and accurate access to selected portions of coronaryartery data. Without using the present system and methods of thedisclosure, quickly selecting, displaying, and analyzing CT images andcoronary artery information, can be cumbersome and inefficient, and maylead to analyst missing critical information in their analysis of apatient's coronary arteries, which may lead to inaccurate evaluation ofa patient's condition.

In various embodiments, the system can identify a patient's coronaryarteries either automatically (e.g., using a machine learning algorithmduring the preprocessing step of set of CT images associated with apatient), or interactively (e.g., by receiving at least some input forma user) by an analyst or practitioner using the system. As describedherein, in some embodiments, the processing of the raw CT scan data cancomprise analysis of the CT data in order to determine and/or identifythe existence and/or nonexistence of certain artery vessels in apatient. As a natural occurring phenomenon, certain arteries may bepresent in certain patients whereas such certain arteries may not existin other patients. In some embodiments, the system can be configured toidentify and label the artery vessels detected in the scan data. Incertain embodiments, the system can be configured to allow a user toclick upon a label of an identified artery within the patient, andthereby allowing that artery to be highlighted in an electronicrepresentation of a plurality of artery vessels existing in the patient.In some embodiments, the system is configured to analyze arteriespresent in the CT scan data and display various views of the arteriespresent in the patient, for example within 10-15 minutes or less. Incontrast, as an example, conducting a visual assessment of a CT toidentify stenosis alone, without consideration of good or bad plaque orany other factor, can take anywhere between 15 minutes to more than anhour depending on the skill level, and can also have substantialvariability across radiologists and/or cardiac imagers.

Although some systems may allow an analyst to view the CT imagesassociated with a patient, they lack the ability to display all of thenecessary views, in real or near real-time, with correspondence between3-D artery tree views of coronary arteries specific to a patient,multiple SMPR views, and a cross-sectional, as well as an axial view, asagittal view, and/or the coronal view. Embodiments of the system can beconfigured this display one or more of the use, or all of the use, whichprovides unparalleled visibility of a patient's coronary arteries, andallows an analyst or practitioner to perceive features and informationthat is simply may not be perceivable without these views. That is, auser interface configured to show all of these views, as well asinformation related to the displayed coronary vessel, allows an analystor practitioner to use their own experience in conjunction with theinformation that the system is providing, to better identify conditionsof the arteries which can help them make a determination on treatmentsfor the patient. In addition, the information that is determined by thesystem and displayed by the user interface that cannot be perceived byan analyst or practitioner is presented in such a manner that is easy tounderstand and quick to assimilate. As an example, the knowledge ofactual radiodensity values of plaque is not something that analyst anddetermine simply by looking at the CT image, but the system can andpresent a full analysis of all plaque is found.

In general, arteries vessels are curvilinear in nature. Accordingly, thesystem can be configured to straighten out such curvilinear arteryvessels into a substantially straight-line view of the artery, and insome embodiments, the foregoing is referred to as a straight multiplanarreformation (MPR) view. In some embodiments, the system is configured toshow a dashboard view with a plurality of artery vessels showing in astraight multiplanar reformation view. In some embodiments, the linearview of the artery vessels shows a cross-sectional view along alongitudinal axis (or the length of the vessel or a long axis) of theartery vessel. In some embodiments, the system can be configured toallow the user to rotate in a 360° fashion about the longitudinal axisof the substantially linear artery vessels in order for the user toreview the vessel walls from various views and angles. In someembodiments, the system is configured to not only show the narrowing ofthe inner vessel diameter but also characteristics of the inner and/orouter vessel wall itself. In some embodiments, the system can beconfigured to display the plurality of artery vessels in a multiplelinear views, e.g., in an SMPR view.

In some embodiments, the system can be configured to display theplurality of artery vessels in a perspective view in order to bettershow the user the curvatures of the artery vessels. In some embodiments,the perspective view is referred to as a curved multiplanar reformationview. In some embodiments, the perspective view comprises the CT imageof the heart and the vessels, for example, in an artery tree view. Insome embodiments, the perspective view comprises a modified CT imageshowing the artery vessels without the heart tissue displayed in orderto better highlight the vessels of the heart. In some embodiments, thesystem can be configured to allow the user to rotate the perspectiveview in order to display the various arteries of the patient fromdifferent perspectives. In some embodiments, the system can beconfigured to show a cross-sectional view of an artery vessel along alatitudinal axis (or the width of the vessel or short axis). In contrastto the cross-sectional view along a longitudinal axis, in someembodiments, the system can allow a user to more clearly see thestenosis or vessel wall narrowing by viewing the artery vessel from across-sectional view across a latitudinal axis.

In some embodiments, the system is configured to display the pluralityof artery vessels in an illustrative view or cartoon view. In theillustrative view of the artery vessels, in some embodiments, the systemcan utilize solid coloring or grey scaling of the specific arteryvessels or sections of specific artery vessels to indicate varyingdegrees of risk for a cardiovascular event to occur in a particularartery vessel or section of artery vessel. For example, the system canbe configured to display a first artery vessel in yellow to indicate amedium risk of a cardiovascular event occurring in the first arteryvessel while displaying a second artery vessel in red to indicate a highrisk of a cardiovascular event occurring in the second artery vessel. Insome embodiments, the system can be configured to allow the user tointeract with the various artery vessels and/or sections of arteryvessels in order to better understand the designated risk associatedwith the artery vessel or section of artery vessel. In some embodiments,the system can allow the user to switch from the illustrative view to aCT view of the arteries of the patient.

In some embodiments, the system can be configured to display in a singledashboard view all or some of the various views described herein. Forexample, the system can be configured to display the linear view withthe perspective view. In another example, the system can be configuredto display the linear view with the illustrative view.

In some embodiments, the processed CT image data can result in allowingthe system to utilize such processed data to display to a user variousarteries of a patient. As described above, the system can be configuredto utilize the processed CT data in order to generate a linear view ofthe plurality of artery vessels of a patient. In some embodiments, thelinear view displays the arteries of a patient as in a linear fashion toresemble a substantially straight line. In some embodiments, thegenerating of the linear view requires the stretching of the image ofone or more naturally occurring curvilinear artery vessels. In someembodiments, the system can be configured to utilize such processed datato allow a user to rotate a displayed linear view of an artery in a 360°rotatable fashion. In some embodiments, the processed CT image data canvisualize and compare the artery morphologies over time, i.e.,throughout the cardiac cycle. The dilation of the arteries, or lackthereof, may represent a healthy versus sick artery that is not capableof vasodilation. In some embodiments, a prediction algorithm can be madeto determine the ability of the artery to dilate or not, by simplyexamining a single point in time.

As mentioned above, aspects of the system can help to visualize apatient's coronary arteries. In some embodiments, the system can beconfigured to utilize the processed data from the raw CT scans in orderto dynamically generate a visualization interface for a user to interactwith and/or analyze the data for a particular patient. The visualizationsystem can display multiple arteries associated with a patient's heart.The system can be configured to display multiple arteries in asubstantially linear fashion even though the arteries are not linearwithin the body of the patient. In some embodiments, the system can beconfigured to allow the user to scroll up and down or left to rightalong the length of the artery in order to visualize different areas ofthe artery. In some embodiments, the system can be configured to allow auser to rotate in a 360° fashion an artery in order to allow the user tosee different portions of the artery at different angles.

Advantageously, the system can be configured to comprise or generatemarkings in areas where there is an amount of plaque buildup thatexceeds a threshold level. In some embodiments, the system can beconfigured to allow the user to target a particular area of the arteryfor further examination. The system can be configured to allow the userto click on one or more marked areas of the artery in order to displaythe underlying data associated with the artery at a particular pointalong the length of the artery. In some embodiments, the system can beconfigured to generate a cartoon rendition of the patient's arteries. Insome embodiments, the cartoon or computer-generated representation ofthe arteries can comprise a color-coded scheme for highlighting certainareas of the patient's arteries for the user to examine further. In someembodiments, the system can be configured to generate a cartoon orcomputer-generated image of the arteries using a red color, or any othergraphical representation, to signify arteries that require furtheranalysis by the user. In some embodiments, the system can label thecartoon representation of the arteries, and the 3D representation of thearteries described above, with stored coronary vessel labels accordingto the labeling scheme. If a user desires, the labeling scheme can bechanged or refined and preferred labels may be stored and used labelcoronary arteries.

In some embodiments, the system can be configured to identify areas inthe artery where ischemia is likely to be found. In some embodiments,the system can be configured to identify the areas of plaque in whichbad plaque exists. In some embodiments, the system can be configured toidentify bad plaque areas by determining whether the coloration and/orthe gray scale level of the area within the artery exceeds a thresholdlevel. In an example, the system can be configured to identify areas ofplaque where the image of a plaque area is black or substantially blackor dark gray. In an example, the system can be configured to identifyareas of “good” plaque by the designation of whiteness or light grey ina plaque area within the artery.

In some embodiments, the system is configured to identify portions of anartery vessel where there is high risk for a cardiac event and/or drawan outline following the vessel wall or profiles of plaque build-upalong the vessel wall. In some embodiments, the system is furtherconfigured to display this information to a user and/or provide editingtools for the user to change the identified portions or the outlinedesignations if the user thinks that the AI algorithm incorrectly drewthe outline designations. In some embodiments, the system comprises anediting tool referred to as “snap-to-lumen,” wherein the user selects aregion of interest by drawing a box around a particular area of thevessel and selecting the snap-to-lumen option and the systemautomatically redraws the outline designation to more closely track theboundaries of the vessel wall and/or the plaque build-up, wherein thesystem is using image processing techniques, such as but not limited toedge detection. In some embodiments, the AI algorithm does not processthe medical image data with complete accuracy and therefore editingtools are necessary to complete the analysis of the medical image data.In some embodiments, the final user editing of the medical image dataallows for faster processing of the medical image data than using solelyAI algorithms to process the medical image data.

In some embodiments, the system is configured to replicate images fromhigher resolution imaging. As an example, in CT, partial volumeartifacts from calcium are a known artifact of CT that results inoverestimation of the volume of calcium and the narrowing of an artery.By training and validating a CT artery appearance to that ofintravascular ultrasound or optical coherence tomography orhistopathology, in some embodiments, the CT artery appearance may bereplicated to be similar to that of IVUS or OCT and, in this way,de-bloom the coronary calcium artifacts to improve the accuracy of theCT image.

In some embodiments, the system is configured to provide a graphicaluser interface for displaying a vessel from a beginning portion to anending portion and/or the tapering of the vessel over the course of thevessel length. Many examples of panels that can be displayed in agraphical user interface are illustrated and described in reference toFIGS. 6A-9N. In some embodiments, portions of the user interface,panels, buttons, or information displayed on the user interface bearranged differently than what is described herein and illustrated inthe Figures. For example, a user may have a preference for arrangingdifferent views of the arteries in different portions of the userinterface.

In some embodiments, the graphical user interface is configured toannotate the displayed vessel view with plaque build-up data obtainedfrom the AI algorithm analysis in order to show the stenosis of thevessel or a stenosis view. In some embodiments, the graphical userinterface system is configured to annotate the displayed vessel viewwith colored markings or other markings to show areas of high risk orfurther analysis, areas of medium risk, and/or areas of low risk. Forexample, the graphical user interface system can be configured toannotate certain areas along the vessel length in red markings, or othergraphical marking, to indicate that there is significant bad fattyplaque build-up and/or stenosis. In some embodiments, the annotatedmarkings along the vessel length are based on one or more variable suchas but not limited to stenosis, biochemistry tests, biomarker tests, AIalgorithm analysis of the medical image data, and/or the like. In someembodiments, the graphical user interface system is configured toannotate the vessel view with an arthrosclerosis view. In someembodiments, the graphical user interface system is configured toannotate the vessel view with an ischemia view. In some embodiments, thegraphical user interface is configured to allow the user to rotate thevessel 180 degrees or 360 degrees in order to display the vessel and theannotated plaque build-up views from different angles. From this view,the user can manually determine the stent length and diameter foraddressing the stenosis, and in some embodiments, the system isconfigured to analyze the medical image information to determine therecommended stent length and diameter, and display the proposed stentfor implantation in the graphical user interface to illustrate to theuser how the stent would address the stenosis within the identified areaof the vessel. In some embodiments, the systems, methods, and devicesdisclosed herein can be applied to other areas of the body and/or othervessels and/or organs of a subject, whether the subject is human orother mammal.

Illustrative Example

One of the main uses of such systems can be to determine the presence ofplaque in vessels, for example but not limited to coronary vessels.Plaque type can be visualized based on Hounsfield Unit density forenhanced readability for the user. Embodiments of the system alsoprovide quantification of variables related to stenosis and plaquecomposition at both the vessel and lesion levels for the segmentedcoronary artery.

In some embodiments, the system is configured as a web-based softwareapplication that is intended to be used by trained medical professionalsas an interactive tool for viewing and analyzing cardiac CT data fordetermining the presence and extent of coronary plaques (i.e.,atherosclerosis) and stenosis in patients who underwent CoronaryComputed Tomography Angiography (CCTA) for evaluation of coronary arterydisease (CAD), or suspected CAD. This system post processes CT imagesobtained using a CT scanner. The system is configured to generate a userinterface that provides tools and functionality for thecharacterization, measurement, and visualization of features of thecoronary arteries.

Features of embodiments of the system can include, for example,centerline and lumen/vessel extraction, plaque composition overlay, useridentification of stenosis, vessel statistics calculated in real time,including vessel length, lesion length, vessel volume, lumen volume,plaque volume (non-calcified, calcified, low-density-non-calcifiedplaque and total), maximum remodeling index, and area/diameter stenosis(e.g., a percentage), two dimensional (2D) visualization of multi-planarreformatted vessel and cross-sectional views, interactive threedimensional (3D) rendered coronary artery tree, visualization of acartoon artery tree that corresponds to actual vessels that appear inthe CT images, semi-automatic vessel segmentation that is usermodifiable, and user identification of stents and Chronic TotalOcclusion (CTO).

In an embodiment, the system uses 18 coronary segments within thecoronary vascular tree (e.g., in accordance with the guidelines of theSociety of Cardiovascular Computed Tomography). The coronary segmentlabels include:

pRCA—proximal right coronary artery

mRCA—mid right coronary artery

dRCA—distal right coronary artery

R-PDA—right posterior descending artery

LM—left main artery

pLAD—proximal left descending artery

mLAD—mid left anterior descending artery

dLAD—distal left anterior descending artery

D1—first diagonal

D2—second diagonal

pCx—proximal left circumflex artery

OM1—first obtuse marginal

LCx—distal left circumflex

OM2—second obtuse marginal

L-PDA—left posterior descending artery

R-PLB—right posterior lateral branch

RI—ramus intermedius artery

L-PLB—left posterior lateral branch

Other embodiments can include more, or fewer, coronary segment labels.The coronary segments present in an individual patient are dependent onwhether they are right or left coronary dominant. Some segments are onlypresent when there is right coronary dominance, and some only when thereis a left coronary dominance. Therefore, in many, if not all instances,no single patient may have all 18 segments. The system will account formost known variants.

In one example of performance of the system, CT scans were processed bythe system, and the resulting data was compared to ground truth resultsproduced by expert readers. Pearson Correlation Coefficients andBland-Altman Agreements between the systems results and the expertreader results is shown in the table below:

Bland-Altman Output Pearson Correlation Agreement Lumen Volume 0.91 96%Vessel Volume 0.93 97% Total Plaque Volume 0.85 95% Calcified PlaqueVolume 0.94 95% Non-Calcified Plaque Volume 0.74 95%Low-Density-Non-Calcified 0.53 97% Plaque Volume

FIGS. 6A-9N illustrate an embodiment of the user interface of thesystem, and show examples of panels, graphics, tools, representations ofCT images, and characteristics, structure, and statistics related tocoronary vessels found in a set of CT images. In various embodiments,the user interface is flexible and that it can be configured to showvarious arrangements of the panels, images, graphics representations ofCT images, and characteristics, structure, and statistics. For example,based on an analyst's preference. The system has multiple menus andnavigational tools to assist in visualizing the coronary arteries.Keyboard and mouse shortcuts can also be used to navigate through theimages and information associated with a set of CT images for patient.

FIG. 6A illustrates an example of a user interface 600 that can begenerated and displayed on a CT image analysis system described herein,the user interface 600 having multiple panels (views) that can showvarious corresponding views of a patient's arteries and informationabout the arteries. In an embodiment, the user interface 600 shown inFIG. 6A can be a starting point for analysis of the patient's coronaryarteries, and is sometimes referred to herein as the “Study Page” (orthe Study Page 600). In some embodiments, the Study Page can include anumber of panels that can be arranged in different positions on the userinterface 600, for example, based on the preference the analyst. Invarious instances of the user interface 600, certain panels of thepossible panels that may be displayed can be selected to be displayed(e.g., based on a user input).

The example of the Study Page 600 shown in FIG. 6A includes a firstpanel 601 (also shown in the circled “2”) including an artery tree 602comprising a three-dimensional (3D) representation of coronary vesselsbased on the CT images and depicting coronary vessels identified in theCT images, and further depicting respective segment labels. Whileprocessing the CT images, the system can determine the extent of thecoronary vessels are determined and the artery tree is generated.Structure that is not part of the coronary vessels (e.g., heart tissueand other tissue around the coronary vessels) are not included in theartery tree 602. Accordingly, the artery tree 602 in FIG. 6A does notinclude any heart tissue between the branches (vessels) 603 of theartery tree 602 allowing visualization of all portions of the arterytree 602 without them being obscured by heart tissue.

This Study Page 600 example also includes a second panel 604 (also shownin the circled “1a”) illustrating at least a portion of the selectedcoronary vessel in at least one straightened multiplanar reformat (SMPR)vessel view. A SMPR view is an elevation view of a vessel at a certainrotational aspect. When multiple SMPR views are displayed in the secondpanel 604 each view can be at a different rotational aspect. Forexample, at any whole degree, or at a half degree, from 0° to 259.5°,where 360° is the same view as 0°. In this example, the second panel 604includes four straightened multiplanar vessels 604 a-d displayed inelevation views at a relative rotation of 0°, 22.5°, 45°, and 67.5°, therotation indicated that the upper portion of the straightenedmultiplanar vessel. In some embodiments, the rotation of each view canbe selected by the user, for example, at the different relative rotationinterval. The user interface includes the rotation tool 605 that isconfigured to receive an input from a user, and can be used to adjustrotation of a SMPR view (e.g., by one or more degrees). One or moregraphics related to the vessel shown in the SMPR view can also bedisplayed. For example, a graphic representing the lumen of the vessel,a graphic representing the vessel wall, and/or a graphic representingplaque.

This Study Page 600 example also includes the third panel 606 (alsoindicated by the circled “1c”), which is configured to show across-sectional view of a vessel 606 a generated based on a CT image inthe set of CT images of the patient. The cross-sectional viewcorresponds to the vessel shown in the SMPR view. The cross-sectionalview also corresponds to a location indicated by a user (e.g., with apointing device) on a vessel in the SMPR view. The user interfacesconfigured such that a selection of a particular location along thecoronary vessel in the second panel 604 displays the associated CT imagein a cross-sectional view in the third panel 606. In this example, agraphic 607 is displayed on the second panel 604 and the third panel 606indicating the extent of plaque in the vessel.

This Study Page 600 example also includes a fourth panel 608 thatincludes anatomical plane views of the selected coronary vessel. In thisembodiment, the Study Page 600 includes an axial plane view 608 a (alsoindicated by the circled “3a”), a coronal plane view 608 b (alsoindicated by the circled “3b”), and a sagittal plane view 608 c (alsoindicated by the circled “3c”). The axial plane view is a transverse or“top” view. The coronal plane view is a front view. The sagittal planeview is a side view. The user interface is configured to displaycorresponding views of the selected coronary vessel. For example, viewsof the selected coronary vessel at a location on the coronary vesselselected by the user (e.g., on one of the SMPR views in the second panel604.

FIG. 6B illustrates another example of the Study Page (user interface)600 that can be generated and displayed on the system, the userinterface 600 having multiple panels that can show various correspondingviews of a patient's arteries. In this example, the user interface 600displays an 3D artery tree in the first panel 601, the cross-sectionalview in the third panel 606, and axial, coronal, and sagittal planeviews in the fourth panel 608. Instead of the second panel 604 shown inFIG. 6A, the user interface 600 includes a fifth panel 609 showingcurved multiplanar reformat (CMPR) vessel views of a selected coronaryvessel. The fifth panel 609 can be configured to show one or more CMPRviews. In this example, two CMPR views were generated and are displayed,a first CMPR view 609 a at 0° and a second CMPR view 609 b at 90°. TheCMPR views can be generated and displayed at various relative rotations,for example, from 0° to 259.5°. The coronary vessel shown in the CMPRview corresponds to the selected vessel, and corresponds to the vesseldisplayed in the other panels. When a location on the vessel in onepanel is selected (e.g., the CMPR view), the views in the other panels(e.g., the cross-section, axial, sagittal, and coronal views) can beautomatically updated to also show the vessel at that the selectedlocation in the respective views, thus greatly enhancing the informationpresented to a user and increasing the efficiency of the analysis.

FIGS. 6C, 6D, and 6E illustrate certain details of a multiplanarreformat (MPR) vessel view in the second panel, and certainfunctionality associated with this view. After a user verifies theaccuracy of the segmentation of the coronary artery tree in panel 602,they can proceed to interact with the MPR views where edits can be madeto the individual vessel segments (e.g., the vessel walls, the lumen,etc.) In the SMPR and CMPR views, the vessel can be rotated inincrements (e.g., 22.5°) by using the arrow icon 605, illustrated inFIGS. 6C and 6D. Alternatively, the vessel can be rotated continuouslyby 1 degree increments in 360 degrees by using the rotation command 610,as illustrated in FIG. 6E. The vessels can also be rotated by pressingthe COMMAND or CTRL button and left clicking+dragging the mouse on theuser interface 600.

FIG. 6F illustrates additional information of the three-dimensional (3D)rendering of the coronary artery tree 602 on the first panel 601 thatallows a user to view the vessels and modify the labels of a vessel.FIG. 6G illustrates shortcut commands for the coronary artery tree 602,axial view 608 a, sagittal view 608 b, and coronal view 608 c. In panel601 shown in FIG. 6F, a user can rotate the artery tree as well as zoomin and out of the 3D rendering using commands selected in the userinterface illustrated in FIG. 6G. Clicking on a vessel will turn ityellow which indicates that is the vessel that is currently beingreviewed. In this view, users can rename or delete a vessel byright-clicking on the vessel name which opens panel 611, which isconfigured to receive an input from a user to rename the vessel. Panel601 also includes a control that can be activated to turn the displayedlabels “on” or “off.” FIG. 6H further illustrates panel 608 of the userinterface for viewing DICOM images in three anatomical planes: axial,coronal, and sagittal. FIG. 6I illustrates panel 606 showing across-sectional view of a vessel. The scroll, zoom in/out, and pancommands can also be used on these views.

FIGS. 6J and 6K illustrate certain aspects of the toolbar 612 and menunavigation functionality of the user interface 600. FIG. 6J illustratesa toolbar of the user interface for navigating the vessels. The toolbar612 includes a button 612 a, 612 b etc. for each of the vesselsdisplayed on the screen. The user interface 600 is configured to displaythe buttons 612 a-n to indicate various information to the user. In anexample, when a vessel is selected, the corresponding button ishighlighted (e.g., displayed in yellow), for example, button 612 c. Inanother example, a button being dark gray with white lettering indicatesthat a vessel is available for analysis. In an example, a button 612 dthat is shaded black means a vessel could not be analyzed by thesoftware because they are either not anatomically present or there aretoo many artifacts. A button 612 e that is displayed as gray with checkmark indicates that the vessel has been reviewed.

FIG. 6K illustrates a view of the user interface 600 with an expandedmenu to view all the series (of images) that are available for reviewand analysis. If the system has provided more than one of the samevessel segment from different series of images for analysis, the userinterface is configured to receive a user input to selected the desiredseries for analysis. In an example, an input can be received indicatinga series for review by a selection on one of the radio buttons 613 fromthe series of interest. The radio buttons will change from gray topurple when it is selected for review. In an embodiment, the software,by default, selects the two series of highest diagnostic quality foranalysis however, all series are available for review. The user can useclinical judgment to determine if the series selected by the system isof diagnostic quality that is required for the analysis, and shouldselect a different series for analysis if desired. The series selectedby the system is intended to improve workflow by prioritizing diagnosticquality images. The system is not intended to replace the user's reviewof all series and selection of a diagnostic quality image within astudy. Users can send any series illustrated in FIG. 6K for the systemto suggest vessel segmentations by hovering the mouse over the seriesand select an “Analyze” button 614 as illustrated in FIG. 6L.

FIG. 6M illustrates a panel that can be displayed on the user interface600 to add a new vessel on the image, according to one embodiment. Toadd a new vessel on the image, the user interface 600 can receive a userinput via a “+Add Vessel” button on the toolbar 612. The user interfacewill display a “create Mode” 615 button appear in the fourth panel 608on the axial, coronal and sagittal view. Then the vessel can be added onthe image by scrolling and clicking the left mouse button to createmultiple dots (e.g., green dots). As the new vessel is being added, itwill preview as a new vessel in the MPR, cross-section, and 3D arterytree view. The user interface is configured to receive a “Done” commandto indicate adding the vessel has been completed. Then, to segment thevessels utilizing the system's semi-automatic segmentation tool, click“Analyze” on the tool bar and the user interface displays suggestedsegmentation for review and modification. The name of the vessel can bechosen by selecting “New” in the 3D artery tree view in the first panel601, which activates the name panel 611 and the name of the vessel canbe selected from panel 611, which then stores the new vessel and itsname. In an embodiment, if the software is unable to identify the vesselwhich has been added by the user, it will return straight vessel linesconnecting the user-added green dots, and the user can adjust thecenterline. The pop-up menu 611 of the user interface allows new vesselsto be identified and named according to a standard format quickly andconsistently.

FIG. 7A illustrates an example of an editing toolbar 714 that includesediting tools which allow users to modify and improve the accuracy ofthe findings resulting from processing CT scans with a machine learningalgorithm, and then processing the CT scans, and information generatedby the machine learning algorithm, by an analyst. In some embodiments,the user interface includes editing tools that can be used to modify andimprove the accuracy of the findings. In some embodiments, the editingtools are located on the left-hand side of the user interface, as shownin FIG. 7A. The following is a listing and description of the availableediting tools. Hovering over each button (icon) will display the name ofeach tool. These tools can be activated and deactivated by clicking onit. If the color of the tool is gray, it is deactivated. If the softwarehas identified any of these characteristics in the vessel, theannotations will already be on the image when the tool is activated. Theediting tools in the toolbar can include one or more of the followingtools: Lumen Wall 701, Snap to Vessel Wall 702, Vessel Wall 703, Snap toLumen Wall 704, Segments 705, Stenosis 706, Plaque Overlay 707,Centerline 708, Chronic Total Occlusion (CTO) 709, Stent 710, Exclude By711, Tracker 712, and Distance 713. The user interface 600 is configuredto activate each of these tools by receiving a user selection on therespective toll icon (shown in the table below and in FIG. 7A) and areconfigured to provide functionality described in the Editing ToolsDescription Table below:

Editing Tools Description Table

LUMEN WALL USERS AN ADJUST OR DRAW NEW LUMEN WALL CONTOURS TO IMPROVETHE ACCURACY OF THE LOCATION AND MEASUREMENTS OF THE LUMEN

SNAP TO USERS CAN DRAG A SHADED AREA AND RELEASE IT IN ORDER TO SNAP THELUMEN VESSEL WALL WALL TO THE VESSEL WALL FOR HEALTHY VESSELS AREAS

VESSEL WALL USERS CAN ADJUST OR DRAW NEW VESSEL WALL CONTOURS TO REFINETHE EXTERIOR OF THE VESSEL WALL

SNAP TO USERS CAN DRAG A SHADED AREA AND RELEASE IT IN ORDER TO SNAP THEVESSEL LUMEN WALL WALL TO THE LUMEN WALL FOR HEALTHY VESSELS AREAS

SEGMENTS USERS CAN ADD SEGMENT MARKERS TO DEFINE THE BOUNDARIES OF EACHOF THE 18 CORONARY SEGMENTS. NEW OR ALREADY EXISTING MARKERS CAN BEDRAGGED UP AND DOWN TO ADJUST TO THE EXACT SEGMENT BOUNDRIES.

STENOSIS THIS TOOL CONSISTS OF 5 MARKERS THAT ALLOW USERS TO MARKREGIONS OF STENOSIS ON THE VESSEL. USERS CAN ADD NEW STENOSIS MARKERSAND NEW OR ALREADY EXISTING MARKERS CAN BE DRAGGED UP/DOWN.

PLAQUE THIS TOOL OVERLAYS THE SMPR AND THE CROSS SECTION VIEWS, WITHCOLORIZED OVERLAY AREAS OF PLAQUE BASED UPON THE PLAQUES HOUNSFIELDATTENUATION

CENTERLINE USERS CAN ADJUST THE CENTERLINE OF THE VESSEL IN THE CMPR ORCROSS-SECTION VIEW ADJUSTMENTS WILL BE PROPAGATED TO THE SMPR VIEW.

CTO CHRONIC TOTAL OCCLUSION TOOL CONSISTS OF TWO MARKERS THAT IDENTIFYTHE START AND END OF A SECTION OF AN ARTERY THAT IS TOTALLY OCCLUDED.MULTIPLE CTOS CAN BE ADDED AND DRAGGED TO THE AREA OF INTEREST.

STENT THE STENT TOOL ALLOW USERS TO IDENTIFY THE PRESENCE OF STENT(S) INTHE CORONAPY ARTERIES. USERS CAN ADD STENT MARKERS AND DRAG EXISTINGMARKERS UP OR DOWN THE EXACT STENT BOUNDARIES.

EXCLUDE BY USING THIS TOOL SECTIONS OF A VESSEL CAN BE REMOVED FROM THEFINAL CALCULATIONS/ANALYSIS. REMOVAL OF THESE SECTIONS IS OFTEN DUE TOTHE PRESENCE OF ARTIFACTS, USUALLY DUE TO MOTION OR MISALIGNMENT ISSUES,AMONG OTHERS.

TRACKER THE TRACKER ORIENTS AN ALLOWS USERS TO CORRELATE THE MPR,CROSS-SECTION, AXIAL, CORONAL, SAGITTAL, AND 3D ARTERY TREE VIEWS.

DISTANCE THE TOOL IS USED ON THE MPR, CROSS-SECTION, AXIAL, CORONAL, ORSAGITTAL VIEWS TO MEASURE DISTANCES BETWEEN POINTS. THE TOOL PROVIDESACCURATE READINGS IN MILLIMETERS ALLOWING FOR QUICK REVIEW ANDESTIMATION ON AREAS OF INTEREST.

FIGS. 7B and 7C illustrate certain functionality of the Tracker tool.The Tracker tool 712 orients and allows user to correlate the viewsshown in the various panels of the user interface 600, for example, inthe SMPR, CMPR, cross-section, axial, coronal, sagittal, and the 3Dartery tree views. To activate, the tracker icon is selected on theediting toolbar. When the Tracker tool 712 is activated, the userinterface generates and displays a line 616 (e.g., a red line) on theSMPR or CMPR view. The system generates on the user interface acorresponding (red) disc 617 which is displayed on the 3D artery tree inthe first panel 601 in a corresponding location as the line 616. Thesystem generates on the user interface a corresponding (red) dot whichhis displayed on the axial, sagittal and coronal views in the fourthpanel 608 in a corresponding location as the line 616. The line 616,disc 617, and dots 618 are location indicators all referencing the samelocation in the different views, such that scrolling any of the trackersup and down will also result in the same movement of the locationindicator in other views. Also, the user interface 600 displays thecross-sectional image in panel 606 corresponding to the locationindicated by the location indicators.

FIGS. 7D and 7E illustrate certain functionality of the vessel and lumenwall tools, which are used to modify the lumen and vessel wall contours.The Lumen Wall tool 701 and the Vessel Wall tool 703 are configured tomodify the lumen and vessel walls (also referred to herein as contours,boundaries, or features) that were previously determined for a vessel(e.g., determined by processing the CT images using a machine learningprocess. These tool are used by the system for determining measurementsthat are output or displayed. By interacting with the contours generatedby the system with these tools, a user can refine the accuracy of thelocation of the contours, and any measurements that are derived fromthose contours. These tools can be used in the SMPR and cross-sectionview. The tools are activated by selecting the vessel and lumen icons701, 703 on the editing toolbar. The vessel wall 619 will be displayedin the MPR view and the cross-section view in a graphical “trace”overlay in a color (e.g., yellow). The lumen wall 629 will be displayedin a graphical “trace” overly in a different color (e.g., purple). In anembodiment, the user interface is configured to refine the contoursthrough interactions with a user. For example, to refine the contours,the user can hover above the contour with a pointing device (e.g.,mouse, stylus, finger) so it highlights the contour, click on thecontour for the desired vessel or lumen wall and drag the displayedtrace to a different location setting a new boundary. The user interface600 is configured to automatically save any changes to these tracings.The system re-calculates any measurements derived from the changescontours in real time, or near real time. Also, the changes made in onepanel on one view are displayed correspondingly in the otherviews/panels.

FIG. 7F illustrates the lumen wall button 701 and the snap to vesselwall button 702 (left) and the vessel wall button 703 and the snap tolumen wall button 704 (right) of the user interface 600 which can beused to activate the Lumen Wall/Snap to Vessel tools 701, 702, and theVessel Wall/Snap to Lumen Wall 703, 704 tools, respectively. The userinterface provides these tools to modify lumen and vessel wall contoursthat were previously determined. The Snap to Vessel/Lumen Wall tools areused to easily and quickly close the gap between lumen and vessel wallcontours, that is, move a trace of the lumen contour and a trace of thevessel contour to be the same, or substantially the same, savinginteractive editing time. The user interface 600 is configured toactivate these tools when a user hovers of the tools with a pointingdevice, which reveals the snap to buttons. For example, hovering overthe Lumen Wall button 701 reveals the Snap to Vessel button 702 to theright-side of the Lumen wall button, and hovering over the Vessel Wallbutton 703 reveals the Snap to Lumen Wall button 704 beside the VesselWall button 703. A button is selected to activate the desired tool. Inreference to Figure G, a pointing device can be used to click at a firstpoint 620 and drag along the intended part of the vessel to edit to asecond point 621, and an area 622 will appear indicating where the toolwill run. Once the end of the desired area 622 is drawn, releasing theselection will snap the lumen and vessel walls together.

FIG. 7H illustrates an example of the second panel 602 that can bedisplayed while using the Segment tool 705 which allows for marking theboundaries between individual coronary segments on the MPR. The userinterface 600 is configured such that when the Segment tool 705 isselected, lines (e.g., lines 623, 624) appear on the vessel image in thesecond panel 602 on the vessels in the SMPR view. The lines indicatesegment boundaries that were determined by the system. The names aredisplayed in icons 625, 626 adjacent to the respective line 623, 624. Toedit the name of the segment, click on an icon 625, 626 and labelappropriately using the name panel 611, illustrated in FIG. 7I. Asegment can also be deleted, for example, by selecting a trashcan icon.The lines 623, 624 can be moved up and down to define the segment ofinterest. If a segment is missing, the user can add a new segment usinga segment addition button, and labeled using the labeling feature in thesegment labeling pop-up menu 611.

FIGS. 7J-7M illustrate an example of using the stenosis tool 706 on theuser interface 600. For example, FIG. 7L illustrates a stenosis buttonwhich can be used to drop stenosis markers based on the user editedlumen and vessel wall contours. FIG. 7M illustrates the stenosis markerson segments on a curved multiplanar vessel (CMPR) view. The second panel604 can be displayed while using the stenosis tool 706 which allows auser to indicate markers to mark areas of stenosis on a vessel. In anembodiment, the stenosis tool contains a set of five markers that areused to mark areas of stenosis on the vessel. These markers are definedas:

R1: Nearest proximal normal slice to the stenosis/lesion

P: Most proximal abnormal slice of the stenosis/lesion

O: Slice with the maximum occlusion

D: Most distal abnormal slice of the stenosis/lesion

R2: Nearest distal normal slice to the stenosis/lesion

In an embodiment, there are two ways to add stenosis markers to themultiplanar view (straightened and curved). After selecting the stenosistool 706, a stenosis can be added by activating the stenosis buttonshown in FIG. 7K or FIG. 7L: to drop 5 evenly spaced stenosis markers(i) click on the Stenosis “+” button (FIG. 7K); (ii) a series of 5evenly spaced yellow lines will appear on the vessel; the user must editthese markers to the applicable position; (iii) move all 5 markers atthe same time by clicking inside the highlighted area encompassed by themarkers and dragging them up/down; (iv) move the individual markers byclicking on the individual yellow lines or tags and move up and down;(v) to delete a stenosis, click on the red trashcan icon. To dropstenosis markers based on the user-edited lumen and vessel wallcontours, click on the stenosis “

” button (see FIG. 7L). A series of 5 yellow lines will appear on thevessel. The positions are based on the user-edited contours. The userinterface 600 provides functionality for a user to edit the stenosismarkers, e.g., can move the stenosis markers FIG. 7J illustrates thestenosis markers R1, P, O, D, and R2 placed on vessels in a SMPR view.FIG. 7M illustrates the markers R1, P, O, D, and R2 placed on vessels ina CMPR view.

FIG. 7N illustrates an example of a panel that can be displayed whileusing the Plaque Overlay tool 707 of the user interface. In anembodiment and in reference to FIG. 7N, “Plaque” is categorized as:low-density-non-calcified plaque (LD-NCP) 701, non-calcified plaque(NCP) 632, or calcified plaque (CP) 633. Selecting the Plaque Overlaytool 707 on the editing toolbar activates the tool. When activated, thePlaque Overlay tool 707 overlays different colors on vessels in the SMPRview in the second panel 604, and in the cross-section the SMPR, andcross-section view in the third panel 606 (see for example, FIG. 7R)with areas of plaque based on Hounsfield Unit (HU) density. In addition,a legend opens in the cross-section view corresponding to plaque type toplaque overlay color as illustrated in FIGS. 7O and 7Q. Users can selectdifferent HU ranges for the three different types of plaque by clickingon the “Edit Thresholds” button located in the top right corner of thecross-section view as illustrated in FIG. 7P. In one embodiment, plaquethresholds default to the values shown in the table below:

Plaque Type Hounsfield Unit (HU) LD-NCP −189 to 30  NCP −189 to 350 CP 350 to 2500

The default values can be revised, if desired, for example, using thePlaque Threshold interface shown in FIG. 7Q. Although default values areprovided, users can select different plaque thresholds based on theirclinical judgment. Users can use the cross-section view of the thirdpanel 606, illustrated in FIG. 7R, to further examine areas of interest.Users can also view the selected plaque thresholds in a vesselstatistics panel of the user interface 600, illustrated in FIG. 7S.

The Centerline tool 708 allows users to adjust the center of the lumen.Changing a center point (of the centerline) may change the lumen andvessel wall and the plaque quantification. if present. The Centerlinetool 708 is activated by selecting it on the user interface 600. A line635 (e.g., a yellow line) will appear on the CMPR view 609 and a point634 (e.g., a yellow point) will appear in the cross-section view on thethird panel 606. The centerline can be adjusted as necessary by clickingand dragging the line/point. Any changes made in the CMPR view will bereflected in the cross-section view, and vice-versa. The user interface600 provides for several ways to extend the centerline of an existingvessel. For example, a user can extend the centerline by: (1)right-clicking on the dot 634 delineated vessel on the axial, coronal,or sagittal view (see FIG. 7U); (2) select “Extend from Start” or“Extend from End” (see FIG. 7U), the view will jump to the start or endof the vessel; (3) add (green) dots to extend the vessel (see FIG. 7V);(4) when finished, select the (blue) check mark button, to cancel theextension, select the (red) “x” button (see for example, FIG. 7V). Theuser interface then extends the vessel according to the changes made bythe user. A user can then manually edit the lumen and vessel walls onthe SMPR or cross-section views (see for example, FIG. 7W). If the userinterface is unable to identify the vessel section which has been addedby the user, it will return straight vessel lines connecting theuser-added dots. The user can then adjust the centerline.

The user interface 600 also provides a Chronic Total Occlusion (CTO)tool 709 to identify portions of an artery with a chronic totalocclusion (CTO), that is, a portion of artery with 100% stenosis and nodetectable blood flow. Since it is likely to contain a large amount ofthrombus, the plaque within the CTO is not included in overall plaquequantification. To activate, click on the CTO tool 709 on the editingtoolbar 612. To add a CTO, click on the CTO “+” button on the userinterface. Two lines (markers) 636, 637 will appear on the MPR view inthe second panel 604, as illustrated in FIG. 7X indicating a portion ofthe vessel of the CTO. The markers 636, 637 can be moved to adjust theextent of the CTO. If more than one CTO is present, additional CTO's canbe added by again activating the CTO “+” button on the user interface. ACTO can also be deleted, if necessary. The location of the CTO isstored. In addition, portions of the vessel that are within thedesignated CTO are not included in the overall plaque calculation, andthe plaque quantification determination is re-calculated as necessaryafter CTO's are identified.

The user interface 600 also provides a Stent tool 710 to indicate wherein vessel a stent exists. The Stent tool is activated by a userselection of the Stent tool 710 on the toolbar 612. To add a stent,click on the Stent “+” button provided on the user interface. Two lines638, 639 (e.g., purple lines) will appear on of the MPR view asillustrated in FIG. 7Y, and the lines 638, 639 can be moved to indicatethe extend of the stent by clicking on the individual lines 638, 639 andmoving them up and down along the vessel to the ends of the stent.Overlapping with the stent (or the CTO/Exclusion/Stenosis) markers isnot permitted by the user interface 600. A stent can also be deleted.

The user interface 600 also provides an Exclude tool 711 that isconfigured to indicate a portion of a vessel to exclude from theanalysis due to blurring caused by motion, contrast, misalignment, orother reasons. Excluding poor quality images will improve the overallquality of the results of the analysis for the non-excluded portions ofthe vessels. To exclude the top or bottom portion of a vessel, activatethe segment tool 705 and the exclude tool 711 in the editing toolbar612. FIG. 7Z illustrates the use of the exclusion tool to exclude aportion from the top of the vessel. FIG. 7AA illustrates the use of theexclusion tool to exclude a bottom portion of the vessel. A firstsegment marker acts as the exclusion marker for the top portion of thevessel. The area enclosed by exclusion markers is excluded from allvessel statistic calculations. An area can be excluded by dragging thetop segment marker to the bottom of the desired area of exclusion. Theexcluded area will be highlighted. Or the “End” marker can be dragged tothe top of the desired area of exclusion. The excluded area will behighlighted, and a user can enter the reason for an exclusion in theuser interface (see FIG. 7AC). To add a new exclusion to the center ofthe vessel, activate the exclude tool 711 on the editing toolbar 612.Click on the Exclusion “+” button. A pop-up window on the user interfacewill appear for the reason of the exclusion (FIG. 7AC), and the reasoncan be entered and it is stored in reference to the indicated excludedarea. Two markers 640, 641 will appear on the MPR as shown in FIG. 7AB.Move both markers at the same time by clicking inside the highlightedarea. The user can move the individual markers by clicking and draggingthe lines 640, 641. The user interface 600 tracks the locations of theof the exclusion marker lines 640, 641 (and previously defined features)and prohibits overlap of the area defined by the exclusion lines 640,641 with any previously indicated portions of the vessel having a CTO,stent or stenosis. The user interface 600 also is configured to delete adesignated exclusion.

Now referring to FIGS. 7AD-7AG, the user interface 600 also provides aDistance tool 713, which is used to measure the distance between twopoints on an image. It is a drag and drop ruler that captures precisemeasurements. The Distance tool works in the MPR, cross-section, axial,coronal, and sagittal views. To activate, click on the distance tool 713on the editing toolbar 612. Then, click and drag between the desired twopoints. A line 642 and measurement 643 will appear on the imagedisplayed on the user interface 600. Delete the measurement byright-clicking on the distance line 642 or measurement 643 and selecting“Remove the Distance” button 644 on the user interface 600 (see FIG.7AF). FIG. 7AD illustrates an example of measuring a distance of astraightened multiplanar vessel (SMPR). FIG. 7AE illustrates an exampleof measuring the distance 642 of a curved multiplanar vessel (CMPR).FIG. 7AF illustrates an example of measuring a distance 642 of across-section of the vessel. FIG. 7AG illustrates an example ofmeasuring the distance 642 on an Axial View of a patient's anatomy.

An example of a vessel statistics panel of the user interface 600 isdescribed in reference to FIGS. 7AH-7AK. FIG. 7AH illustrates a “vesselstatistics” portion 645 of the user interface 600 (e.g., a button) of apanel which can be selected to display the vessel statistics panel 646(or “tab”), illustrated in FIG. 7AI. FIG. 7AJ illustrates certainfunctionality on the vessel statistics tab that allows a user to clickthrough the details of multiple lesions. FIG. 7AK further illustratesthe vessel panel which the user can use to toggle between vessels. Forexample, Users can hide the panel by clicking on the “X” on the topright hand side of the panel, illustrated in FIG. 7AI. Statistics areshown at the per-vessel and per-lesion (if present) level, as indicatedin FIG. 7AJ.

If more than one lesion is marked by the user, the user can clickthrough each lesion's details. To view the statistics for each vessel,the users can toggle between vessels on the vessel panel illustrated inFIG. 7AK.

General information pertaining to the length and volume are presentedfor the vessel and lesion (if present) in the vessel statistics panel646, along with the plaque and stenosis information on a per-vessel andper-lesion level. Users may exclude artifacts from the image they do notwant to be considered in the calculations by using the exclusion tool.The following tables indicate certain statistics that are available forvessels, lesions, plaque, and stenosis.

Vessel Term Definition Vessel Length (mm) Length of a linear coronaryvessel Total Vessel Volume (mm3) The volume of consecutive slices ofvessel contours. Total Lumen Volume (mm3) The volume of consecutiveslices of lumen contours

Lesion Term Definition Lesion Length (mm) Linear distance from the startof a coronary lesion to the end of a coronary lesion. Vessel Volume(mm3) The volume of consecutive slices of vessel contours. Lumen Volume(mm3) The volume of consecutive slices of lumen contours.

Plaque Term Definition Total Calcified Plaque Volume (mm3) Calcifiedplaque is defined as plaque in between the lumen and vessel wall with anattenuation of greater than 350 HU, or as defined by the user, and isreported in absolute measures by plaque volume. Calcified plaques areidentified in each coronary artery ≥1.5 mm in mean vessel diameter.Total Non-Calcified Plaque Volume (mm3) Non-calcified plaque is definedas plaque in between the lumen and vessel wall with an attenuation ofless than or equal to 350, or as defined by the user, HU and is reportedin absolute measures by plaque volume. The total non-calcified plaquevolume is the sum total of all non-calcified plaques identified in eachcoronary artery ≥1.5 mm in mean vessel diameter. Non-calcified plaquedata reported is further broken down into low-density plaque, based onHU density thresholds. Low-Density Non-Calcified Plaque VolumeLow-Density-Non-Calcified Plaque is (mm3) defined as plaque in betweenthe lumen and vessel wall with an attenuation of less than or equal to30 HU or as defined by the user and is reported in absolute measures byplaque volume. Total Plaque Volume (mm³) Plaque volume is defined asplaque in between the lumen and vessel wall reported in absolutemeasures. The total plaque volume is the sum total of all plaqueidentified in each coronary artery ≥1.5 mm in mean vessel diameter orwherever the user places the “End” marker.

Stenosis Term Definition Remodeling Index Remodeling Index is defined asthe mean vessel diameter at a denoted slice divided by the mean vesseldiameter at a reference slice. Greatest Diameter The deviation of themean lumen diameter at Stenosis (%) the denoted slice from a referenceslice, expressed in percentage. Greatest Area The deviation of the lumenarea at the Stenosis (%) denoted slice to a reference area, expressed inpercentage

A quantitative variable that is used in the system and displayed onvarious portions of the user interface 600, for example, in reference tolow-density non-calcified plaque, non-calcified plaque, and calcifiedplaque, is the Hounsfield unit (HU). As is known, a Hounsfield Unitscale is a quantitative scale for describing radiation, and isfrequently used in reference to CT scans as a way to characterizeradiation attenuation and thus making it easier to define what a givenfinding may represent. A Hounsfield Unit measurement is presented inreference to a quantitative scale. Examples of Hounsfield Unitmeasurements of certain materials are shown in the following table:

Material HU Air −1000  Fat −50 Distilled Water  0 Soft Tissue +40 Blood+40 to 80 Calcified Plaques 350-1000+ Bone +1000 

In an embodiment, information that the system determines relating tostenosis, atherosclerosis, and CAD-RADS details are included on panel800 of the user interface 600, as illustrated in FIG. 8A. By default,the CAD-RADS score may be unselected and requires the user to manuallyselect the score on the CAD-RADS page. Hovering over the “#” iconscauses the user interface 600 to provide more information about theselected output. To view more details about the stenosis,atherosclerosis, and CAD-RADS outputs, click the “View Details” buttonin the upper right of panel 800—this will navigate to the applicabledetails page. In an embodiment, in the center of a centerpiece page viewof the user interface 600 there is a non-patient specific rendition of acoronary artery tree 805 (a “cartoon artery tree” 805) broken intosegments 805 a-855 r based on the SCCT coronary segmentation, asillustrated in panel 802 in FIG. 8C. All analyzed vessels are displayedin color according to the legend 806 based on the highest diameterstenosis within that vessel. Greyed out segments/vessels in the cartoonartery tree 805, for example, segment 805 q and 805 r, were notanatomically available or not analyzed in the system (all segments maynot exist in all patients). Per-territory and per-segment informationcan be viewed by clicking the territory above the tree (RCA, LM+LAD,etc.) using, for example, the user interface 600 selection buttons inpanel 801, as illustrated in FIGS. 8B and 8C. Or my selecting a segment805 a-805 r within the cartoon coronary tree 805.

Stenosis and atherosclerosis data displayed on the user interface inpanel 807 will update accordingly as various segments are selected, asillustrated in FIG. 8D. FIG. 8E illustrates an example of a portion ofthe per-territory summary panel 807 of the user interface. FIG. 8F alsoillustrates an example of portion of panel 807 showing the SMPR of aselected vessel and its associated statistics along the vessel atindicated locations (e.g., at locations indicated by a pointing deviceas it is moved along the SMPR visualization). That is, the userinterface 600 is configured to provide plaque details and stenosisdetails in an SMPR visualization in panel 809 and a pop-up panel 810that displays information as the user interface receives locationinformation long the displayed vessel from the user, e.g., via apointing device. The presence of a chronic total occlusion (CT)) and/ora stent are indicated at the vessel segment level. For example, FIG. 8Gillustrates the presence of a stent in the D1 segment. FIG. 8H indicatesthe presence of a CTO in the mRCA segment. Coronary dominance and anyanomalies can be displayed below the coronary artery tree as illustratedin FIG. 8I. The anomalies that were selected in the analysis can bedisplayed, for example, by “hovering” with a pointing device over the“details” button. If plaque thresholds were changed in the analysis, analert can be displayed on the user interface, or on a generated report,that indicates the plaque thresholds were changed. When anomalies arepresent, the coronary vessel segment 805 associated with each anomalywill appear detached from the aorta as illustrated in FIG. 8J. In anembodiment, a textual summary of the analysis can also be displayedbelow the coronary tree, for example, as illustrated in the panel 811 inFIG. 8K.

FIG. 9A illustrates an atherosclerosis panel 900 that can be displayedon the user interface, which displays a summary of atherosclerosisinformation based on the analysis. FIG. 9B illustrates the vesselselection panel which can be used to select a vessel such that thesummary of atherosclerosis information is displayed on a per segmentbasis. The top section of the atherosclerosis panel 900 containsper-patient data, as illustrated in FIG. 9A. When a user “hovers’ overthe “Segments with Calcified Plaque” on panel 901, or hovers over the“Segments with Non-Calcified Plaque” in panel 902, the segment nameswith the applicable plaque are displayed. Below the patient specificdata, users may access per-vessel and per-segment atherosclerosis databy clicking on one of the vessel buttons, illustrated in FIG. 9B.

FIG. 9C illustrates a panel 903, that can be generated and displayed onthe user interface, which shows atherosclerosis information determinedby the system on a per segment basis. The presence of positiveremodeling, the highest remodeling index, and the presence ofLow-Density-Non-Calcified Plaque are reported for each segment in thepanel 903 illustrated in FIG. 9C. For example, plaque data can bedisplayed below on a per-segment basis, and plaque composition volumescan be displayed on a per-segment in the panel 903 illustrated in FIG.9C.

FIG. 9D illustrates a panel 904 that can be displayed on the userinterface that contains stenosis per patient data. The top section ofthe stenosis panel 904 contains per-patient data. Further details abouteach count can be displayed by hovering with a pointing device over thenumbers, as illustrated in FIG. 9E. Vessels included in each territoryare shown in the table below:

Vessel Territory Segment Name LM (Left Main Artery) LM LAD (LeftAnterior Descending) pLAD mLAD dLAD D1 D2 RI LCx (Left CircumflexArtery) pCx LCx OM1 OM2 L-PLB L-PDA RCA (Right Coronary Artery) pRCAmRCA dRCA R-PLB R-PDA

In an embodiment, a percentage Diameter Stenosis bar graph 906 can begenerated and displayed in a panel 905 of the user interface, asillustrated in FIG. 9F. The percentage Diameter Stenosis bar graph 906displays the greatest diameter stenosis in each segment. If a CTO hasbeen marked on the segment, it will display as a 100% diameter stenosis.If more than one stenosis has been marked on a segment, the highestvalue outputs are displayed by default and the user can click into eachstenosis bar to view stenosis details and interrogate smaller stenosis(if present) within that segment. The user can also scroll through eachcross-section by dragging the grey button in the center of a SMPR viewof the vessel, and view the lumen diameter and % diameter stenosis ateach cross-section at any selected location, as illustrated in FIG. 9G.

FIG. 9H illustrates a panel showing categories of the one or morestenosis marked on the SMPR based on the analysis. Color can be used toenhance the displayed information. In an example, stenosis in theLM>=50% diameter stenosis are marked in red. As illustrated in a panel907 of the user interface in FIG. 9I, for each segment's greatestpercentage diameter stenosis the minimum luminal diameter and lumendiameter at the reference can be displayed when a pointing device is“hovered” above the graphical vessel cross-section representation, asillustrated in FIG. 9J. If a segment was not analyzed or is notanatomically present, the segment will be greyed out and will display“Not Analyzed”. If a segment was analyzed but did not have any stenosismarked, the value will display “N/A”.

FIG. 9K illustrates a panel 908 of the user interface that indicatesCADS-RADS score selection. The CAD-RADS panel displays the definitionsof CAD-RADS as defined by “Coronary Artery Disease—Reporting and DataSystem (CAD-RADS) An Expert Consensus Document of SCCT, ACR and NASCI.Endorsed by the ACC”. The user is in full control of selecting theCAD-RADS score. In an embodiment, no score will be suggested by thesystem. In another embodiment, a CAD-RADS score can be suggested. Once aCAD-RADS score is selected on this page, the score will display in bothcertain user interface panels and full text report pages. Once aCAD-RADS score is selected, the user has the option of selectingmodifiers and the presentation of symptoms. Once a presentation isselected, the interpretation, further cardiac investigation andmanagement guidelines can be displayed to the user on the userinterface, for example, as illustrated in the panel 909 illustrated inFIG. 9L. These guidelines reproduce the guidelines found in “CoronaryArtery Disease—Reporting and Data System (CAD-RADS) An Expert ConsensusDocument of SCCT, ACR and NASCI. Endorsed by the ACC.”

FIGS. 9M and 9N illustrate tables that can be generated and displayed ona panel of the user interface, and/or included in a report. FIG. 9Millustrates quantitative stenosis and vessel outputs. FIG. 9Nillustrates quantitative plaque outputs. In these quantitative tables, auser can view quantitative per-segment stenosis and atherosclerosisoutputs from the system analysis. The quantitative stenosis and vesseloutputs table (FIG. 9M) includes information for the evaluated arteriesand segments. Totals are given for each vessel territory. Informationcan include, for example, length, vessel volume, lumen volume, totalplaque volume, maximum diameter stenosis, maximum area stenosis, andhighest remodeling index. The quantitative plaque outputs table (FIG.9N) includes information for the evaluated arteries and segments.Information can include, for example, total plaque volume, totalcalcified plaque volume, non-calcified plaque volume, low-densitynon-calcified plaque volume, and total non-calcified plaque volume. Theuser is also able to download a PDF or CSV file of the quantitativeoutputs is a full text Report. The full text Report presents a textualsummary of the atherosclerosis, stenosis, and CAD-RADS measures. Theuser can edit the report, as desired. Once the user chooses to edit thereport, the report will not update the CAD-RADS selection automatically.

FIG. 10 is a flowchart illustrating a process 1000 for analyzing anddisplaying CT images and corresponding information. At block 1005, theprocess 1000 stores computer-executable instructions, a set of CT imagesof a patient's coronary vessels, vessel labels, and artery informationassociated with the set of CT images including information of stenosis,plaque, and locations of segments of the coronary vessels. All of thesteps of the process can be performed by embodiments of the systemdescribed herein, for example, on embodiments of the systems describedin FIG. 13. For example, by one or more computer hardware processors incommunication with the one or more non-transitory computer storagemediums, executing the computer-executable instructions stored on one ormore non-transitory computer storage mediums. In various embodiments,the user interface can include one or more portions, or panels, that areconfigured to display one or more of images, in various views (e.g.,SMPR, CMPR, cross-sectional, axial, sagittal, coronal, etc.) related tothe CT images of a patient's coronary arteries, a graphicalrepresentation of coronary arteries, features (e.g., a vessel wall, thelumen, the centerline, the stenosis, plaque, etc.) that have beenextracted or revised by machine learning algorithm or by an analyst, andinformation relating to the CT images that has been determined by thesystem, by an analyst, or by an analyst interacting with the system(e.g., measurements of features in the CT images. In variousembodiments, panels of the user interface can be arranged differentlythan what is described herein and what is illustrated in thecorresponding figures. A user can make an input to the user interfaceusing a pointing device or a user's finger on a touchscreen. In anembodiment, the user interface can receive input by determining theselection of a button/icon/portion of the user interface. In anembodiment, the user interface can receive an input in a defined fieldof the user interface.

At block 1010, the process 1000 can generate and display in a userinterface a first panel including an artery tree comprising athree-dimensional (3D) representation of coronary vessels based on theCT images and depicting coronary vessels identified in the CT images,and depicting segment labels, the artery tree not including heart tissuebetween branches of the artery tree. An example of such an artery tree602 is shown in panel 601 in FIG. 6A. In various embodiments, panel 601can be positioned in locations of the user interface 600 other than whatis shown in FIG. 6A.

At block 1015, the process 1000 can receive a first input indicating aselection of a coronary vessel in the artery tree in the first panel.For example, the first input can be received by the user interface 600of a vessel in the artery tree 602 in panel 601. At block 1020, inresponse to the first input, the process 1000 can generate and displayon the user interface a second panel illustrating at least a portion ofthe selected coronary vessel in at least one straightened multiplanarvessel (SMPR) view. In an example, the SMPR view is displayed in panel604 of FIG. 6A.

At block 1025, the process 1000 can generate and display on the userinterface a third panel showing a cross-sectional view of the selectedcoronary vessel, the cross-sectional view generated using one of the setof CT images of the selected coronary vessel. Locations along the atleast one SMPR view are each associated with one of the CT images in theset of CT images such that a selection of a particular location alongthe coronary vessel in the at least one SMPR view displays theassociated CT image in the cross-sectional view in the third panel. Inan example, the cross-sectional view can be displayed in panel 606 asillustrated in FIG. 6A. At block 1030, the process 1000 can receive asecond input on the user interface indicating a first location along theselected coronary artery in the at least one SMPR view. In an example,user may use a pointing device to select a different portion of thevessel shown in the SMPR view in panel 604. At block 1030, the process1000, in response to the second input, displays the associated CT scanassociated in the cross-sectional view in the third panel, panel 606.That is, the cross-sectional view that correspond to the first input isreplaced by the cross-sectional view that corresponds to the secondinput on the SMPR view.

Normalization Device

In some instances, medical images processed and/or analyzed as describedthroughout this application can be normalized using a normalizationdevice. As will be described in more detail in this section, thenormalization device may comprise a device including a plurality ofsamples of known substances that can be placed in the medical imagefield of view so as to provide images of the known substances, which canserve as the basis for normalizing the medical images. In someinstances, the normalization device allows for direct within imagecomparisons between patient tissue and/or other substances (e.g.,plaque) within the image and known substances within the normalizationdevice.

As mentioned briefly above, in some instances, medical imaging scannersmay produce images with different scalable radiodensities for the sameobject. This, for example, can depend not only on the type of medicalimaging scanner or equipment used but also on the scan parameters and/orenvironment of the particular day and/or time when the scan was taken.As a result, even if two different scans were taken of the same subject,the brightness and/or darkness of the resulting medical image may bedifferent, which can result in less than accurate analysis resultsprocessed from that image. To account for such differences, in someembodiments, the normalization device comprising one or more knownsamples of known materials can be scanned together with the subject, andthe resulting image of the one or more known elements can be used as abasis for translating, converting, and/or normalizing the resultingimage.

Normalizing the medical images that will be analyzed can be beneficialfor several reasons. For example, medical images can be captured under awide variety of conditions, all of which can affect the resultingmedical images. In instances where the medical imager comprises a CTscanner, a number of different variables can affect the resulting image.Variable image acquisition parameters, for example, can affect theresulting image. Variable image acquisition parameters can comprise oneor more of a kilovoltage (kV), kilovoltage peak (kVp), a milliamperage(mA), or a method of gating, among others. In some embodiments, methodsof gating can include prospective axial triggering, retrospective ECGhelical gating, and fast pitch helical, among others. Varying any ofthese parameters, may produce slight differences in the resultingmedical images, even if the same subject is scanned.

Additionally, the type of reconstruction used to prepare the image afterthe scan may provide differences in medical images. Example types ofreconstruction can include iterative reconstruction, non-iterativereconstruction, machine learning-based reconstruction, and other typesof physics-based reconstruction among others. FIGS. 11A-11D illustratedifferent images reconstructed using different reconstructiontechniques. In particular, FIG. 11A illustrates a CT image reconstructedusing filtered back projection, while FIG. 11B illustrates the same CTimage reconstructed using iterative reconstruction. As shown, the twoimages appear slightly different. The normalization device describedbelow can be used to help account for these differences by providing amethod for normalizing between the two. FIG. 11C illustrates a CT imagereconstructed by using iterative reconstruction, while FIG. 11Dillustrates the same image reconstructed using machine learning. Again,one can see that the images include slight differences, and thenormalization device described herein can advantageously be useful innormalizing the images to account for the two differences.

As another example, various types of image capture technologies can beused to capture the medical images. In instances where the medicalimager comprises a CT scanner, such image capture technologies mayinclude a dual source scanner, a single source scanner, dual energy,monochromatic energy, spectral CT, photon counting, and differentdetector materials, among others. As before, images captured usingdifference parameters may appear slightly different, even if the samesubject is scanned. In addition to CT scanners, other types of medicalimagers can also be used to capture medical images. These can include,for example, x-ray, ultrasound, echocardiography, intravascularultrasound (IVUS), MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS). Use of the normalization device can facilitatenormalization of images such that images captured on these differentimaging devices can be used in the methods and systems described herein.

Additionally, new types of medical imaging technologies are currentlybeing developed. Use of the normalization device can allow the methodsand systems described herein to be used even with medical imagingtechnologies that are currently being developed or that will bedeveloped in the future. Use of different or emerging medical imagingtechnologies can also cause slight differences between images.

Another factor that can cause differences in medical images that can beaccounted for using the normalization device can be use of differentcontrast agents during medical imaging. Various contrast agentscurrently exist, and still others are under development. Use of thenormalization device can facilitate normalization of medical imagesregardless of the type of contrast agent used and even in instanceswhere no contrast agent is used.

These slight differences can, in some instances, negatively impactanalysis of the image, especially where analysis of the image isperformed by artificial intelligence or machine learning algorithms thatwere trained or developed using medical images captured under differentconditions. In some embodiments, the methods and systems describedthroughout this application for analyzing medical images can include theuse of artificial intelligence and/or machine learning algorithms. Suchalgorithms can be trained using medical images. In some embodiments, themedical images that are used to train these algorithms can include thenormalization device such that the algorithms are trained based onnormalized images. Then, by normalizing subsequent images by alsoincluding the normalization device in those images, the machine learningalgorithms can be used to analyze medical images captured under a widevariety of parameters, such as those described above.

In some embodiments, the normalization device described herein isdistinguishable from a conventional phantom. In some instances,conventional phantoms can be used to verify if a CT machine is operatingin a correct manner. These conventional phantoms can be usedperiodically to verify the calibration of the CT machine. For example,in some instances, conventional phantoms can be used prior to each scan,weekly, monthly, yearly, or after maintenance on the CT machine toensure proper functioning and calibration. Notably, however, theconventional phantoms do not provide a normalization function thatallows for normalization of the resulting medical images acrossdifferent machines, different parameters, different patients, etc.

In some embodiments, the normalization device described herein canprovide this functionality. The normalization device can allow for thenormalization of CT data or other medical imaging data generated byvarious machine types and/or for normalization across differentpatients. For example, different CT devices manufactured by variousmanufacturers, can produce different coloration and/or different grayscale images. In another example, some CT scanning devices can producedifferent coloration and/or different gray scale images as the CTscanning device ages or as the CT scanning device is used or based onthe environmental conditions surrounding the device during the scanning.In another example, patient tissue types or the like can cause differentcoloration and/or gray scale levels to appear differently in medicalimage scan data. Normalization of CT scan data can be important in orderto ensure that processing of the CT scan data or other medical imagingdata is consistent across various data sets generated by variousmachines or the same machines used at different times and/or acrossdifferent patients. In some embodiments, the normalization device needsto be used each time a medical image scan is performed because scanningequipment can change over time and/or patients are different with eachscan. In some embodiments, the normalization device is used inperforming each and every scan of patient in order to normalize themedical image data of each patient for the AI algorithm(s) used toanalyze the medical image data of the patient. In other words, in someembodiments, the normalization device is used to normalize to eachpatient as opposed to each scanner. In some embodiments, thenormalization device may have different known materials with differentdensities adjacent to each other (e.g., as described with reference toFIG. 12F). This configuration may address an issue present in some CTimages where the density of a pixel influences the density of theadjacent pixels and that influence changes with the density of each ofthe individual pixel. One example of such an embodiment can includedifferent contrast densities in the coronary lumen influencing thedensity of the plaque pixels. The normalization device can address thisissue by having known volumes of known substances to help to correctlyevaluate volumes of materials/lesions within the image correcting insome way the influence of the blooming artifact on quantitative CT imageanalysis/measures. In some instances, the normalization device mighthave moving known materials with known volume and known and controllablemotion. This may allow to exclude or reduce the effect of motion onquantitative CT image analysis/measures.

Accordingly, the normalization device, in some embodiments, is not aphantom in the traditional sense because the normalization device is notjust calibrating to a particular scanner but is also normalizing for aspecific patient at a particular time in a particular environment for aparticular scan, for particular scan image acquisition parameters,and/or for specific contrast protocols. Accordingly, in someembodiments, the normalization device can be considered a reversephantom. This can be because, rather than providing a mechanism forvalidating a particular medical imager as a conventional phantom would,the normalization device can provide a mechanism for normalizing orvalidating a resulting medical image such that it can be compared withother medical images taken under different conditions. In someembodiments, the normalization device is configured to normalize themedical image data being examined with the medical image data used totrain, test, and/or validate the AI algorithms used for analyzing the tobe examined medical image data.

In some embodiments, the normalization of medical scanning data can benecessary for the AI processing methods disclosed herein because in someinstances AI processing methods can only properly process medicalscanning data when the medical scanning data is consistent across allmedical scanning data being processed. For example, in situations wherea first medical scanner produces medical images showing fatty materialas dark gray or black, whereas a second medical scanner produces medicalimage showing the same fatty material as medium or light gray, then theAI processing methodologies of the systems, methods, and devicesdisclosed herein may misidentify and/or not fully identify the fattymaterials in one set or both sets of the medical images produced by thefirst and second medical scanners. This can be even more problematic asthe relationship of specific material densities may not be not constant,and even may change in an non linear way depending on the material andon the scanning parameters. In some embodiments, the normalizationdevice enables the use of AI algorithms trained on certain medicalscanner devices to be used on medical images generated bynext-generation medical scanner devices that may have not yet even beendeveloped.

FIG. 12A is a block diagram representative of an embodiment of anormalization device 1200 that can be configured to normalize medicalimages for use with the methods and systems described herein. In theillustrated embodiment, the normalization device 1200 can include asubstrate 1202. The substrate 1202 can provide the body or structure forthe normalization device 1200. In some embodiments, the normalizationdevice 1200 can comprise a square or rectangular or cube shape, althoughother shapes are possible. In some embodiments, the normalization device1200 is configured to be bendable and/or be self-supporting. Forexample, the substrate 1202 can be bendable and/or self-supporting. Abendable substrate 1202 can allow the normalization device to fit to thecontours of a patient's body. In some embodiments, the substrate 1202can comprise one or more fiducials 1203. The fiducials 1203 can beconfigured to facilitate determination of the alignment of thenormalization device 1200 in an image of the normalization device suchthat the position in the image of each of the one or more compartmentsholding samples of known materials can be determined.

The substrate 1202 can also include a plurality of compartments (notshown in FIG. 12A, but see, for example, compartments 1216 of FIGS.12C-12F). The compartments 1216 can be configured to hold samples ofknown materials, such as contrast samples 1204, studied variable samples1206, and phantom samples 1208. In some embodiments, the contrastsamples 1204 comprise samples of contrast materials used during captureof the medical image. In some embodiments, the samples of the contrastmaterials 1204 comprise one or more of iodine, Gad, Tantalum, Tungsten,Gold, Bismuth, or Ytterbium. These samples can be provided within thecompartments 1216 of the normalization device 1200 at variousconcentrations. The studied variable samples 1206 can includes samplesof materials representative of materials to be analyzed systems andmethods described herein. In some examples, the studied variable samples1206 comprise one or more of calcium 1000 HU, calcium 220 HU, calcium150 HU, calcium 130 HU, and a low attenuation (e.g., 30 HU) material.Other studied variable samples 1206 provided at different concentrationscan also be included. In general, the studied variable samples 1206 cancorrespond to the materials for which the medical image is beinganalyzed. The phantom samples 1208 can comprise samples of one or morephantom materials. In some examples, the phantom samples 1208 compriseone or more of water, fat, calcium, uric acid, air, iron, or blood.Other phantom samples 1208 can also be used.

In some embodiments, the more materials contained in the normalizationdevice 1200, or the more compartments 1216 with different materials inthe normalization device 1200, the better the normalization of the dataproduced by the medical scanner. In some embodiments, the normalizationdevice 1200 or the substrate 1202 thereof is manufactured from flexibleand/or bendable plastic. In some embodiments, the normalization device1200 is adapted to be positioned within or under the coils of an MRscanning device. In some embodiments, the normalization device 1200 orthe substrate 1202 thereof is manufactured from rigid plastic.

In the illustrated embodiment of FIG. 12A, the normalization device 1200also includes an attachment mechanism 1210. The attachment mechanism1210 can be used to attach the normalization device 1200 to the patient.For example, in some embodiments, the normalization device 1200 isattached to the patient near the coronary region to be imaged prior toimage acquisition. In some embodiments, the normalization device 1200can be adhered to the skin of a patient using an adhesive or Velcro orsome other fastener or glue. In some embodiments, the normalizationdevice 1200 can be applied to a patient like a bandage. For example, insome embodiments, a removable Band-Aid or sticker is applied to the skinof the patient, wherein the Band-Aid can comprise a Velcro outwardfacing portion that allows the normalization device having acorresponding Velcro mating portion to adhere to the Band-Aid or stickerthat is affixed to the skin of the patient (see, for example, thenormalization device of FIG. 12G, described below).

In some embodiments, the attachment mechanism 1210 can be omitted, suchthat the normalization device 1200 need not be affixed to the patient.Rather, in some embodiments, the normalization device can be placed in amedical scanner with or without a patient. In some embodiments, thenormalization device can be configured to be placed alongside a patientwithin a medical scanner.

In some embodiments, the normalization device 1200 can be a reusabledevice or be a disposable one-time use device. In some embodiments, thenormalization device 1200 comprises an expiration date, for example, thedevice can comprise a material that changes color to indicate expirationof the device, wherein the color changes over time and/or after acertain number of scans or an amount of radiation exposure (see, forexample, FIGS. 12H and 12I, described below). In some embodiments, thenormalization device 1200 requires refrigeration between uses, forexample, to preserve one or more of the samples contained therein. Insome embodiments, the normalization device 1200 can comprise anindicator, such as a color change indicator, that notifies the user thatthe device has expired due to heat exposure or failure to refrigerate.

In certain embodiments, the normalization device 1200 comprises amaterial that allows for heat transfer from the skin of the patient inorder for the materials within the normalization device 1200 to reachthe same or substantially the same temperature of the skin of thepatient because in some cases the temperature of the materials canaffect the resulting coloration or gray-scale of the materials producedby the image scanning device. For example, the substrate 1202 cancomprise a material with a relatively high heat transfer coefficient tofacilitate heat transfer from the patient to the samples within thesubstrate 1202. In some embodiments, the normalization device 1200 canbe removably coupled to a patient's skin by using an adhesive that canallow the device to adhere to the skin of a patient.

In some embodiments, the normalization device 1200 can be used in theimaging field of view or not in the imaging field of view. In someembodiments, the normalization device 1200 can be imaged simultaneouslywith the patient image acquisition or sequentially. Sequential use cancomprise first imaging the normalization device 1200 and the imaging thepatient shortly thereafter using the same imaging parameters (or viceversa). In some embodiments, the normalization device 1200 can be staticor programmed to be in motion or movement in sync with the imageacquisition or the patient's heart or respiratory motion. In someembodiments, the normalization device 1200 can utilize comparison toimage domain-based data or projection domain-based data. In someembodiments, the normalization device 1200 can be a 2D (area), or 3D(volume), or 4D (changes with time) device. In some embodiments, two ormore normalization devices 1200 can be affixed to and/or positionedalongside a patient during medical image scanning in order to accountfor changes in coloration and/or gray scale levels at different depthswithin the scanner and/or different locations within the scanner.

In some embodiments, the normalization device 1200 can comprise one ormore layers, wherein each layer comprises compartments for holding thesame or different materials as other layers of the device. FIG. 12B, forexample, illustrates a perspective view of an embodiment of anormalization device 1200 including a multilayer substrate 1202. in theillustrated embodiment, the substrate 1202 comprises a first layer 1212and a second layer 1214. The second layer 1214 can be positioned abovethe first layer 1212. In other embodiments, one or more additionallayers may be positioned above the second layer 1214. Each of the layers1212, 1214 can be configured with compartments for holding the variousknown samples, as shown in FIG. 12C. In some embodiments, the variouslayers 1212, 1214 of the normalization device 1200 allow fornormalization at various depth levels for various scanning machines thatperform three-dimensional scanning, such as MR and ultrasound. In someembodiments, the system can be configured to normalize by averaging ofcoloration and/or gray scale level changes in imaging characteristicsdue to changes in depth.

FIG. 12C is a cross-sectional view of the normalization device 1200 ofFIG. 12B illustrating various compartments 1216 positioned therein forholding samples of known materials for use during normalization. Thecompartments 1216 can be configured to hold, for example, the contrastsamples 1204, the studied variable samples 1206, and the phantom samples1208 illustrated in FIG. 12A. The compartments 1216 may comprise spaces,pouches, cubes, spheres, areas, or the like, and within each compartment1216 there is contained one or more compounds, fluids, substances,elements, materials, and the like. In some embodiments, each of thecompartments 1216 can comprise a different substance or material. Insome embodiments, each compartment 1216 is air-tight and sealed toprevent the sample, which may be a liquid, from leaking out.

Within each layer 1212, 1214, or within the substrate 1202, thenormalization device 1200 may include different arrangements for thecompartments 1216. FIG. 12D illustrates a top down view of an examplearrangement of a plurality of compartments 1216 within the normalizationdevice 1200. In the illustrated embodiment, the plurality ofcompartments 1216 are arranged in a rectangular or grid-like pattern.FIG. 12E illustrates a top down view of another example arrangement of aplurality of compartments 1216 within a normalization device 1200. Inthe illustrated embodiment, the plurality of compartments 1216 arearranged in a circular pattern. Other arrangements are also possible.

FIG. 12F is a cross-sectional view of another embodiment of anormalization device 1200 illustrating various features thereof,including adjacently arranged compartments 1216A, self-sealing fillablecompartments 1216B, and compartments of various sizes and shapes 1216C.As shown in FIG. 12F, one or more of the compartments 1216A can bearranged so as to be adjacent to each other so that materials within thecompartments 1216A can be in contact with and/or in close proximity tothe materials within the adjacent compartments 1216A. In someembodiments, the normalization device 1200 comprises high densitymaterials juxtaposed to low density materials in order to determine howa particular scanning device displays certain materials, therebyallowing normalization across multiple scanning devices. In someembodiments, certain materials are positioned adjacent or near othermaterials because during scanning certain materials can influence eachother. Examples of materials that can be placed in adjacently positionedcompartments 1216A can include iodine, air, fat material, tissue,radioactive contrast agent, gold, iron, other metals, distilled water,and/or water, among others.

In some embodiments, the normalization device 1200 is configured receivematerial and/or fluid such that the normalization device isself-sealing. Accordingly, FIG. 12F illustrates compartments 1216B thatare self-sealing. These can allow a material to be injected into thecompartment 1216B and then sealed therein. For example, a radioactivecontrast agent can be injected in a self-sealing manner into acompartment 1216B of the normalization device 1200, such that themedical image data generated from the scanning device can be normalizedover time as the radioactive contrast agent decays over time during thescanning procedure. In some embodiments, the normalization device can beconfigured to contain materials specific for a patient and/or a type oftissue being analyzed and/or a disease type and/or a scanner machinetype.

In some embodiments, the normalization device 1200 can be configuredmeasure scanner resolution and type of resolution by configuring thenormalization device 1200 with a plurality of shapes, such as a circle.Accordingly, the compartments 1216C can be provided with differentshapes and sizes. FIG. 12F illustrates an example wherein compartments1216C are provided with different shapes (cubic and spherical) anddifferent sizes. In some embodiments, all compartments 1216 can be thesame shape and size.

In some embodiments, the size of one or more compartment 1216 of thenormalization device 1200 can be configured or selected to correspond tothe resolution of the medical image scanner. For example, in someembodiments, if the spatial resolution of a medical image scanner is 0.5mm×0.5 mm×0.5 mm, then the dimension of the compartments of thenormalization device can also be 0.5 mm×0.5 mm×0.5 mm. In someembodiments, the sizes of the compartments range from 0.5 mm to 0.75 mm.In some embodiments, the width of the compartments of the normalizationdevice can be about 0.1 mm, about 0.15 mm, about 0.2 mm, about 0.25 mm,about 0.3 mm, about 0.35 mm, about 0.4 mm, about 0.45 mm, about 0.5 mm,about 0.55 mm, about 0.6 mm, about 0.65 mm, about 0.7 mm, about 0.75 mm,about 0.8 mm, about 0.85 mm, about 0.9 mm, about 0.95 mm, about 1.0 mm,and/or within a range defined by two of the aforementioned values. Insome embodiments, the length of the compartments of the normalizationdevice can be about 0.1 mm, about 0.15 mm, about 0.2 mm, about 0.25 mm,about 0.3 mm, about 0.35 mm, about 0.4 mm, about 0.45 mm, about 0.5 mm,about 0.55 mm, about 0.6 mm, about 0.65 mm, about 0.7 mm, about 0.75 mm,about 0.8 mm, about 0.85 mm, about 0.9 mm, about 0.95 mm, about 1.0 mm,and/or within a range defined by two of the aforementioned values. Insome embodiments, the height of the compartments of the normalizationdevice can be about 0.1 mm, about 0.15 mm, about 0.2 mm, about 0.25 mm,about 0.3 mm, about 0.35 mm, about 0.4 mm, about 0.45 mm, about 0.5 mm,about 0.55 mm, about 0.6 mm, about 0.65 mm, about 0.7 mm, about 0.75 mm,about 0.8 mm, about 0.85 mm, about 0.9 mm, about 0.95 mm, about 1.0 mm,and/or within a range defined by two of the aforementioned values.

In some embodiments, the dimensions of each of the compartments 1216 inthe normalization device 1200 are the same or substantially the same forall of the compartments 1216. In some embodiments, the dimensions ofsome or all of the compartments 1216 in the normalization device 1200can be different from each other in order for a single normalizationdevice 1200 to have a plurality of compartments having differentdimensions such that the normalization device 1200 can be used invarious medical image scanning devices having different resolutioncapabilities (for example, as illustrated in FIG. 12F). In someembodiments, a normalization device 1200 having a plurality ofcompartments 1216 with differing dimensions enable the normalizationdevice to be used to determine the actual resolution capability of thescanning device. In some embodiments, the size of each compartment 1216may extend up to 10 mm, and the sizes of each compartment may bevariable depending upon the material contained within.

In the illustrated embodiment of FIGS. 12C and 12F, the normalizationdevice 1200 includes an attachment mechanism 1210 which includes anadhesive surface 1218. The adhesive surface 1218 can be configured toaffix (e.g., removably affix) the normalization device 1200 to the skinof the patient. FIG. 12G is a perspective view illustrating anembodiment of an attachment mechanism 1210 for a normalization device1200 that uses hook and loop fasteners 1220 to secure a substrate of thenormalization device to a fastener of the normalization device 1200. Inthe illustrated embodiment, an adhesive surface 1218 can be configuredto be affixed to the patient. The adhesive surface 1218 can include afirst hook and loop fastener 1220. A corresponding hook and loopfastener 1220 can be provided on a lower surface of the substrate 1202and used to removably attach the substrate 1202 to the adhesive surface1218 via the hook and loop fasteners 1220.

FIGS. 12H and 12I illustrate an embodiment of a normalization device1200 that includes an indicator 1222 configured to indicate anexpiration status of the normalization device 1200. The indicator 1222can comprise a material that changes color or reveals a word to indicateexpiration of the device, wherein the color or text changes or appearsover time and/or after a certain number of scans or an amount ofradiation exposure. FIG. 12H illustrates the indicator 1222 in a firststate representative of a non-expired state, and FIG. 12I illustratesthe indicator 1222 in a second state representative of an expired state.In some embodiments, the normalization device 1200 requiresrefrigeration between uses. In some embodiments, the indicator 1222,such as a color change indicator, can notify the user that the devicehas expired due to heat exposure or failure to refrigerate.

In some embodiments, the normalization device 1200 can be used with asystem configured to set distilled water to a gray scale value of zero,such that if a particular medical image scanning device registers thecompartment of the normalization device 1200 comprising distilled wateras having a gray scale value of some value other than zero, then thesystem can utilize an algorithm to transpose or transform the registeredvalue to zero. In some embodiments, the system is configured to generatea normalization algorithm based on known values established forparticular substances in the compartments of the normalization device1200, and on the detected/generated values by a medical image scanningdevice for the same substances in the compartments 1216 of thenormalization device 1200. In some embodiments, the normalization device1200 can be configured to generate a normalization algorithm based on alinear regression model to normalize medical image data to be analyzed.In some embodiments, the normalization device 1200 can be configured togenerate a normalization algorithm based on a non-linear regressionmodel to normalize medical image data to be analyzed. In someembodiments, the normalization device 1200 can be configured to generatea normalization algorithm based on any type of model or models, such asan exponential, logarithmic, polynomial, power, moving average, and/orthe like, to normalize medical image data to be analyzed. In someembodiments, the normalization algorithm can comprise a two-dimensionaltransformation. In some embodiments, the normalization algorithm cancomprise a three-dimensional transformation to account for other factorssuch as depth, time, and/or the like.

By using the normalization device 1200 to scan known substances usingdifferent machines or the same machine at different times, the systemcan normalize CT scan data across various scanning machines and/or thesame scanning machine at different times. In some embodiments, thenormalization device 1200 disclosed herein can be used with any scanningmodality including but not limited to x-ray, ultrasound, echocardiogram,magnetic resonance (MR), optical coherence tomography (OCT),intravascular ultrasound (IVUS) and/or nuclear medicine imaging,including positron-emission tomography (PET) and single photon emissioncomputed tomography (SPECT).

In some embodiments, the normalization device 1200 contains one or morematerials that form plaque (e.g., studied variable samples 1206) and oneor more materials that are used in the contrast that is given to thepatient through a vein during examination (e.g., contrast samples 1204).In some embodiments, the materials within the compartments 1216 includeiodine of varying concentrations, calcium of varying densities,non-calcified plaque materials or equivalents of varying densities,water, fat, blood or equivalent density material, iron, uric acid, air,gadolinium, tantalum, tungsten, gold, bismuth, ytterbium, and/or othermaterial. In some embodiments, the training of the AI algorithm can bebased at least in part on data relating to the density in the images ofthe normalization device 1200. As such, in some embodiments, the systemcan have access to and/or have stored pre-existing data on how thenormalization device 1200 behaved or was shown in one or more imagesduring the training of the AI algorithm. In some embodiments, the systemcan use such prior data as a baseline to determine the difference withhow the normalization device 1200 behaves in the new or current CT scanto which the AI algorithm is applied to. In some embodiments, thedetermined difference can be used to calibrate, normalize, and/or mapone or more densities in recently acquired image(s) to one or moreimages that were obtained and/or used during training of the AIalgorithm.

As a non-limiting example, in some embodiments, the normalization device1200 comprises calcium. If, for example, the calcium in the CT ornormalization device 1200 that was used to train the AI algorithm(s)showed a density of 300 Hounsfield Units (HU), and if the same calciumshowed a density of 600 HU in one or more images of a new scan, then thesystem, in some embodiments, may be configured to automatically divideall calcium densities in half to normalize or transform the new CTimage(s) to be equivalent to the old CT image(s) used to train the AIalgorithm.

In some embodiments, as discussed above, the normalization device 1200comprises a plurality or all materials that may be relevant, which canbe advantageous as different materials can change densities in differentamounts across scans. For example, if the density of calcium changes 2×across scans, the density of fat may change around 10% across the samescans. As such, it can be advantageous for the normalization device 1200to comprise a plurality of materials, such as for example one or morematerials that make up plaque, blood, contrast, and/or the like.

As described above, in some embodiments, the system can be configured tonormalize, map, and/or calibrate density readings and/or CT imagesobtained from a particular scanner and/or subject proportionallyaccording to changes or differences in density readings and/or CT imagesobtained from one or more materials of a normalization device 1200 usinga baseline scanner compared to density readings and/or CT imagesobtained from one or more same materials of a normalization device 1200using the particular scanner and/or subject. As a non-limiting example,for embodiments in which the normalization device 1200 comprisescalcium, the system can be configured to apply the same change indensity of known calcium between the baseline scan and the new scan, forexample 2×, to all other calcium readings of the new scan to calibrateand/or normalize the readings.

In some embodiments, the system can be configured to normalize, map,and/or calibrate density readings and/or CT images obtained from aparticular scanner and/or subject by averaging changes or differencesbetween density readings and/or CT images obtained from one or morematerials of a normalization device 1200 using a baseline scannercompared to density readings and/or CT images obtained from one or morematerials or areas of a subject using the same baseline scanner. As anon-limiting example, for embodiments in which the normalization device1200 comprises calcium, the system can be configured to determine adifference, or a ratio thereof, in density readings between calcium inthe normalization device 1200 and other areas of calcium in the subjectduring the baseline scan. In some embodiments, the system can beconfigured to similarly determine a difference, or a ratio thereof, indensity readings between calcium in the normalization device 1200 andother areas of calcium in the subject during the new scan; dividing thevalue of calcium from the device to the value of calcium anywhere elsein the image can cancel out any change as the difference in conditionscan affect the same material in the same manner.

In some embodiments, the device will account for scan parameters (suchas mA or kVp), type and number of x-ray sources within a scanner (suchas single source or dual source), temporal resolution of a scanner,spatial resolution of scanner or image, image reconstruction method(such as adaptive statistical iterative reconstruction, model-basediterative reconstruction, machine learning-based iterativereconstruction or similar); image reconstruction method (such as fromdifferent types of kernels, overlapping slices from retrospectiveECG-helical studies, non-overlapping slices from prospective axialtriggered studies, fast pitch helical studies, or half vs. full scanintegral reconstruction); contrast density accounting for internalfactors (such as oxygen, blood, temperature, and others); contrastdensity accounting for external factors (such as contrast density,concentration, osmolality and temporal change during the scan);detection technology (such as material, collimation and filtering);spectral imaging (such as polychromatic, monochromatic and spectralimaging along with material basis decomposition and single energyimaging); photon counting; and/or scanner brand and model.

In some embodiments, the normalization device 1200 can be applied to MRIstudies, and account for one or more of: type of coil; place ofpositioning, number of antennas; depth from coil elements; imageacquisition type; pulse sequence type and characteristics; fieldstrength, gradient strength, slew rate and other hardwarecharacteristics; magnet vendor, brand and type; imaging characteristics(thickness, matrix size, field of view, acceleration factor,reconstruction methods and characteristics, 2D, 3D, 4D [cine imaging,any change over time], temporal resolution, number of acquisitions,diffusion coefficients, method of populating k-space); contrast(intrinsic [oxygen, blood, temperature, etc.] and extrinsic types,volume, temporal change after administration); static or movingmaterials; quantitative imaging (including T1 T2 mapping, ADC,diffusion, phase contrast, and others); and/or administration ofpharmaceuticals during image acquisition.

In some embodiments, the normalization device 1200 can be applied toultrasound studies, and account for one or more of: type and machinebrands; transducer type and frequency; greyscale, color, and pulsed wavedoppler; B- or M-mode doppler type; contrast agent; field of view; depthfrom transducer; pulsed wave deformity (including elastography), angle;imaging characteristics (thickness, matrix size, field of view,acceleration factor, reconstruction methods and characteristics, 2D, 3D,4D [cine imaging, any change over time]; temporal resolution; number ofacquisitions; gain, and/or focus number and places, amongst others.

In some embodiments, the normalization device 1200 can be applied tonuclear medicine studies, such as PET or SPECT and account for one ormore of: type and machine brands; for PET/CT all CT applies; for PET/MRall MR applies; contrast (radiopharmaceutical agent types, volume,temporal change after administration); imaging characteristics(thickness, matrix size, field of view, acceleration factor,reconstruction methods and characteristics, 2D, 3D, 4D [cine imaging,any change over time]; temporal resolution; number of acquisitions;gain, and/or focus number and places, amongst others.

In some embodiments, the normalization device may have different knownmaterials with different densities adjacent to each other. This mayaddress any issue present in some CT images where the density of a pixelinfluences the density of the adjacent pixels and that influence changeswith the density of each of the individual pixel. One example of thisembodiment being different contrast densities in the coronary lumeninfluencing the density of the plaque pixels. In some embodiments, thenormalization device may include known volumes of known substances tohelp to correctly evaluate volumes of materials/lesions within the imagein order to correct the influence of the blooming artifact onquantitative CT image analysis/measures. In some embodiments, thenormalization device might have moving known materials with known volumeand known and controllable motion. This would allow to exclude or reducethe effect of motion on quantitative CT image analysis/measures.

In some embodiments, having a known material on the image in thenormalization device might also be helpful for material specificreconstructions from the same image. For example, it can be possible touse only one set of images to display only known materials, not needingmultiple kV/spectral image hardware.

FIG. 12J is a flowchart illustrating an example method 1250 fornormalizing medical images for an algorithm-based medical imaginganalysis such as the analyses described herein. Use of the normalizationdevice can improve accuracy of the algorithm-based medical imaginganalysis. The method 1250 can be a computer-implemented method,implemented on a system that comprises a processor and an electronicstorage medium. The method 1250 illustrates that the normalizationdevice can be used to normalize medical images captured under differentconditions. For example, at block 1252, a first medical image of acoronary region of a subject and the normalization device is accessed.The first medical image can be obtained non-invasively. Thenormalization device can comprise a substrate comprising a plurality ofcompartments, each of the plurality of compartments holding a sample ofa known material, for example as described above. At block 1254, asecond medical image of a coronary region of a subject and thenormalization device is captured. The second medical image can beobtained non-invasively. Although the method 1250 is described withreference to a coronary region of a patient, the method is alsoapplicable to all body parts and not only the vessels as the sameprinciples apply to all body parts, all time points and all imagingdevices. This can even include “live” type of images such as fluoroscopyor MR real time image.

As illustrated by the portion within the dotted lines, the first medicalimage and the second medical image can comprise at least one of thefollowing: (1) one or more first variable acquisition parametersassociated with capture of the first medical image differ from acorresponding one or more second variable acquisition parametersassociated with capture of the second medical image, (2) a first imagecapture technology used to capture the first medical image differs froma second image capture technology used to capture the second medicalimage, and (3) a first contrast agent used during the capture of thefirst medical image differs from a second contrast agent used during thecapture of the second medical image.

In some embodiments, the first medical image and the second medicalimage each comprise a CT image and the one or more first variableacquisition parameters and the one or more second variable acquisitionparameters comprise one or more of a kilovoltage (kV), kilovoltage peak(kVp), a milliamperage (mA), or a method of gating. In some embodiments,the method of gating comprises one of prospective axial triggering,retrospective ECG helical gating, and fast pitch helical. In someembodiments, the first image capture technology and the second imagecapture technology each comprise one of a dual source scanner, a singlesource scanner, dual energy, monochromatic energy, spectral CT, photoncounting, and different detector materials. In some embodiments, thefirst contrast agent and the second contrast agent each comprise one ofan iodine contrast of varying concentration or a non-iodine contrastagent. In some embodiments, the first image capture technology and thesecond image capture technology each comprise one of CT, x-ray,ultrasound, echocardiography, intravascular ultrasound (IVUS), MRimaging, optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS).

In some embodiments, a first medical imager that captures the firstmedical imager is different than a second medical image that capture thesecond medical image. In some embodiments, the subject of the firstmedical image is different than the subject of the first medical image.In some embodiments, wherein the subject of the first medical image isthe same as the subject of the second medical image. In someembodiments, wherein the subject of the first medical image is differentthan the subject of the second medical image. In some embodiments,wherein the capture of the first medical image is separated from thecapture of the second medical image by at least one day. In someembodiments, wherein the capture of the first medical image is separatedfrom the capture of the second medical image by at least one day. Insome embodiments, wherein a location of the capture of the first medicalimage is geographically separated from a location of the capture of thesecond medical image.

Accordingly, it is apparent that the first and second medical images canbe acquired under different conditions that can cause differencesbetween the two images, even if the subject of each image is the same.The normalization device can help to normalize and account for thesedifferences.

The method 1250 then moves to blocks 1262 and 1264, at which imageparameters of the normalization device within the first medical imageand which image parameters of the normalization device within the secondmedical image are identified, respectively. Due to differentcircumstances under which the first and second medical images werecaptured, the normalization device may appear differently in each image,even though the normalization device includes the same known samples.

Next, at blocks 1266 and 1268, the method generates a normalized firstmedical image for the algorithm-based medical imaging analysis based inpart on the first identified image parameters of the normalizationdevice within the first medical image and generates a normalized secondmedical image for the algorithm-based medical imaging analysis based inpart on the second identified image parameters of the normalizationdevice within the second medical image, respectively. In these blocks,each image is normalized based on the appearance or determinedparameters of the normalization device in each image.

In some embodiments, the algorithm-based medical imaging analysiscomprises an artificial intelligence or machine learning imaginganalysis algorithm, and the artificial intelligence or machine learningimaging analysis algorithm was trained using images that included thenormalization device.

System Overview

In some embodiments, the systems, devices, and methods described hereinare implemented using a network of one or more computer systems, such asthe one illustrated in FIG. 13. FIG. 13 is a block diagram depicting anembodiment(s) of a system for medical image analysis, visualization,risk assessment, disease tracking, treatment generation, and/or patientreport generation.

As illustrated in FIG. 13, in some embodiments, a main server system1302 is configured to perform one or more processes, analytics, and/ortechniques described herein, some of which relating to medical imageanalysis, visualization, risk assessment, disease tracking, treatmentgeneration, and/or patient report generation. In some embodiments, themain server system 1302 is connected via an electronic communicationsnetwork 1308 to one or more medical facility client systems 1304 and/orone or more user access point systems 1306. For example, in someembodiments, one or more medical facility client systems 1304 can beconfigured to access a medical image taken at the medical facility of asubject, which can then be transmitted to the main server system 1302via the network 1308 for further analysis. After analysis, in someembodiments, the analysis results, such as for example quantified plaqueparameters, assessed risk of a cardiovascular event, generated report,annotated and/or derived medical images, and/or the like, can betransmitted back to the medical facility client system 1304 via thenetwork 1308. In some embodiments, the analysis results, such as forexample quantified plaque parameters, assessed risk of a cardiovascularevent, generated report, annotated and/or derived medical images, and/orthe like, can be transmitted also to a user access point system 1306,such as a smartphone or other computing device of the patient orsubject. As such, in some embodiments, a patient can be allowed to viewand/or access a patient-specific report and/or other analyses generatedand/or derived by the system from the medical image on the patient'scomputing device.

In some embodiments, the main server system 1302 can comprise and/or beconfigured to access one or more modules and/or databases for performingthe one or more processes, analytics, and/or techniques describedherein. For example, in some embodiments, the main server system 1302can comprise an image analysis module 1310, a plaque quantificationmodule 1312, a fat quantification module 1314, an atherosclerosis,stenosis, and/or ischemia analysis module 1316, a visualization/GUImodule 1318, a risk assessment module 1320, a disease tracking module1322, a normalization module 1324, a medical image database 1326, aparameter database 1328, a treatment database 1330, a patient reportdatabase 1332, a normalization device database 1334, and/or the like.

In some embodiments, the image analysis module 1310 can be configured toperform one or more processes described herein relating to imageanalysis, such as for example vessel and/or plaque identification from araw medical image. In some embodiments, the plaque quantification module1312 can be configured to perform one or more processes described hereinrelating to deriving or generating quantified plaque parameters, such asfor example radiodensity, volume, heterogeneity, and/or the like ofplaque from a raw medical image. In some embodiments, the fatquantification module 1314 can be configured to perform one or moreprocesses described herein relating to deriving or generating quantifiedfat parameters, such as for example radiodensity, volume, heterogeneity,and/or the like of fat from a raw medical image. In some embodiments,the atherosclerosis, stenosis, and/or ischemia analysis module 1316 canbe configured to perform one or more processes described herein relatingto analyzing and/or generating an assessment or quantification ofatherosclerosis, stenosis, and/or ischemia from a raw medical image. Insome embodiments, the visualization/GUI module 1318 can be configured toperform one or more processes described herein relating to deriving orgenerating one or more visualizations and/or GUIs, such as for example astraightened view of a vessel identifying areas of good and/or badplaque from a raw medical image. In some embodiments, the riskassessment module 1320 can be configured to perform one or moreprocesses described herein relating to deriving or generating riskassessment, such as for example of a cardiovascular event or diseasefrom a raw medical image. In some embodiments, the disease trackingmodule 1322 can be configured to perform one or more processes describedherein relating to tracking a plaque-based disease, such as for exampleatherosclerosis, stenosis, ischemia, and/or the like from a raw medicalimage. In some embodiments, the normalization module 1324 can beconfigured to perform one or more processes described herein relating tonormalizing and/or translating a medical image, for example based on amedical image of a normalization device comprising known materials, forfurther processing and/or analysis.

In some embodiments, the medical image database 1326 can comprise one ormore medical images that are used for one or more of the variousanalysis techniques and processes described herein. In some embodiments,the parameter database 1328 can comprise one or more parameters derivedfrom raw medical images by the system, such as for example one or morevessel morphology parameters, quantified plaque parameters, quantifiedfat parameters, and/or the like. In some embodiments, the treatmentdatabase 1328 can comprise one or more recommended treatments derivedfrom raw medical images by the system. In some embodiments, the patientreport database 1332 can comprise one or more patient-specific reportsderived from raw medical images by the system and/or one or morecomponents thereof that can be used to generate a patient-specificreport based on medical image analysis results. In some embodiments, thenormalization database 1334 can comprise one or more historical datapoints and/or datasets of normalizing various medical images and/or thespecific types of medical imaging scanners and/or specific scanparameters used to obtain those images, as well as previously usednormalization variables and/or translations for different medicalimages.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 14. The example computer system 1402 is incommunication with one or more computing systems 1420 and/or one or moredata sources 1422 via one or more networks 1418. While FIG. 14illustrates an embodiment of a computing system 1402, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1402 may be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1402 can comprise a Medical Analysis, RiskAssessment, and Tracking Module 1414 that carries out the functions,methods, acts, and/or processes described herein. The Medical Analysis,Risk Assessment, and Tracking Module 1414 is executed on the computersystem 1402 by a central processing unit 1406 discussed further below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C or C++, PYPHON or the like. Software modules may becompiled or linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted language such asBASIC, PERL, LUA, or Python. Software modules may be called from othermodules or from themselves, and/or may be invoked in response todetected events or interruptions. Modules implemented in hardwareinclude connected logic units such as gates and flip-flops, and/or mayinclude programmable units, such as programmable gate arrays orprocessors.

Generally, the modules described herein refer to logical modules thatmay be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and may be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses may befacilitated through the use of computers. Further, in some embodiments,process blocks described herein may be altered, rearranged, combined,and/or omitted.

The computer system 1402 includes one or more processing units (CPU)1406, which may comprise a microprocessor. The computer system 1402further includes a physical memory 1410, such as random access memory(RAM) for temporary storage of information, a read only memory (ROM) forpermanent storage of information, and a mass storage device 1404, suchas a backing store, hard drive, rotating magnetic disks, solid statedisks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory,diskette, or optical media storage device. Alternatively, the massstorage device may be implemented in an array of servers. Typically, thecomponents of the computer system 1402 are connected to the computerusing a standards based bus system. The bus system can be implementedusing various protocols, such as Peripheral Component Interconnect(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) andExtended ISA (EISA) architectures.

The computer system 1402 includes one or more input/output (I/O) devicesand interfaces 1412, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 1412 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 1412 can alsoprovide a communications interface to various external devices. Thecomputer system 1402 may comprise one or more multi-media devices 1408,such as speakers, video cards, graphics accelerators, and microphones,for example.

The computer system 1402 may run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 1402 may run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 1402 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, SunOS, Solaris, MacOS, or other compatible operatingsystems, including proprietary operating systems. Operating systemscontrol and schedule computer processes for execution, perform memorymanagement, provide file system, networking, and I/O services, andprovide a user interface, such as a graphical user interface (GUI),among other things.

The computer system 1402 illustrated in FIG. 14 is coupled to a network1418, such as a LAN, WAN, or the Internet via a communication link 1416(wired, wireless, or a combination thereof). Network 1418 communicateswith various computing devices and/or other electronic devices. Network1418 is communicating with one or more computing systems 1420 and one ormore data sources 1422. The Medical Analysis, Risk Assessment, andTracking Module 1414 may access or may be accessed by computing systems1420 and/or data sources 1422 through a web-enabled user access point.Connections may be a direct physical connection, a virtual connection,and other connection type. The web-enabled user access point maycomprise a browser module that uses text, graphics, audio, video, andother media to present data and to allow interaction with data via thenetwork 1418.

Access to the Medical Analysis, Risk Assessment, and Tracking Module1414 of the computer system 1402 by computing systems 1420 and/or bydata sources 1422 may be through a web-enabled user access point such asthe computing systems' 1420 or data source's 1422 personal computer,cellular phone, smartphone, laptop, tablet computer, e-reader device,audio player, or other device capable of connecting to the network 1418.Such a device may have a browser module that is implemented as a modulethat uses text, graphics, audio, video, and other media to present dataand to allow interaction with data via the network 1418.

The output module may be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module may be implemented to communicate with inputdevices 1412 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module may communicate with a set ofinput and output devices to receive signals from the user.

The input device(s) may comprise a keyboard, roller ball, pen andstylus, mouse, trackball, voice recognition system, or pre-designatedswitches or buttons. The output device(s) may comprise a speaker, adisplay screen, a printer, or a voice synthesizer. In addition a touchscreen may act as a hybrid input/output device. In another embodiment, auser may interact with the system more directly such as through a systemterminal connected to the score generator without communications overthe Internet, a WAN, or LAN, or similar network.

In some embodiments, the system 1402 may comprise a physical or logicalconnection established between a remote microprocessor and a mainframehost computer for the express purpose of uploading, downloading, orviewing interactive data and databases on-line in real time. The remotemicroprocessor may be operated by an entity operating the computersystem 1402, including the client server systems or the main serversystem, and/or may be operated by one or more of the data sources 1422and/or one or more of the computing systems 1420. In some embodiments,terminal emulation software may be used on the microprocessor forparticipating in the micro-mainframe link.

In some embodiments, computing systems 1420 who are internal to anentity operating the computer system 1402 may access the MedicalAnalysis, Risk Assessment, and Tracking Module 1414 internally as anapplication or process run by the CPU 1406.

The computing system 1402 may include one or more internal and/orexternal data sources (for example, data sources 1422). In someembodiments, one or more of the data repositories and the data sourcesdescribed above may be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 1402 may also access one or more databases 1422. Thedatabases 1422 may be stored in a database or data repository. Thecomputer system 1402 may access the one or more databases 1422 through anetwork 1418 or may directly access the database or data repositorythrough I/O devices and interfaces 1412. The data repository storing theone or more databases 1422 may reside within the computer system 1402.

In some embodiments, one or more features of the systems, methods, anddevices described herein can utilize a URL and/or cookies, for examplefor storing and/or transmitting data or user information. A UniformResource Locator (URL) can include a web address and/or a reference to aweb resource that is stored on a database and/or a server. The URL canspecify the location of the resource on a computer and/or a computernetwork. The URL can include a mechanism to retrieve the networkresource. The source of the network resource can receive a URL, identifythe location of the web resource, and transmit the web resource back tothe requestor. A URL can be converted to an IP address, and a DomainName System (DNS) can look up the URL and its corresponding IP address.URLs can be references to web pages, file transfers, emails, databaseaccesses, and other applications. The URLs can include a sequence ofcharacters that identify a path, domain name, a file extension, a hostname, a query, a fragment, scheme, a protocol identifier, a port number,a username, a password, a flag, an object, a resource name and/or thelike. The systems disclosed herein can generate, receive, transmit,apply, parse, serialize, render, and/or perform an action on a URL.

A cookie, also referred to as an HTTP cookie, a web cookie, an internetcookie, and a browser cookie, can include data sent from a websiteand/or stored on a user's computer. This data can be stored by a user'sweb browser while the user is browsing. The cookies can include usefulinformation for websites to remember prior browsing information, such asa shopping cart on an online store, clicking of buttons, logininformation, and/or records of web pages or network resources visited inthe past. Cookies can also include information that the user enters,such as names, addresses, passwords, credit card information, etc.Cookies can also perform computer functions. For example, authenticationcookies can be used by applications (for example, a web browser) toidentify whether the user is already logged in (for example, to a website). The cookie data can be encrypted to provide security for theconsumer. Tracking cookies can be used to compile historical browsinghistories of individuals. Systems disclosed herein can generate and usecookies to access data of an individual. Systems can also generate anduse JSON web tokens to store authenticity information, HTTPauthentication as authentication protocols, IP addresses to tracksession or identity information, URLs, and the like.

EXAMPLE EMBODIMENTS

The following are non-limiting examples of certain embodiments ofsystems and methods of characterizing coronary plaque. Other embodimentsmay include one or more other features, or different features, that arediscussed herein.

Embodiment 1: A computer-implemented method of quantifying andclassifying coronary plaque within a coronary region of a subject basedon non-invasive medical image analysis, the method comprising:accessing, by a computer system, a medical image of a coronary region ofa subject, wherein the medical image of the coronary region of thesubject is obtained non-invasively; identifying, by the computer systemutilizing a coronary artery identification algorithm, one or morecoronary arteries within the medical image of the coronary region of thesubject, wherein the coronary artery identification algorithm isconfigured to utilize raw medical images as input; identifying, by thecomputer system utilizing a plaque identification algorithm, one or moreregions of plaque within the one or more coronary arteries identifiedfrom the medical image of the coronary region of the subject, whereinthe plaque identification algorithm is configured to utilize raw medicalimages as input; determining, by the computer system, one or morevascular morphology parameters and a set of quantified plaque parametersof the one or more identified regions of plaque from the medical imageof the coronary region of the subject, wherein the set of quantifiedplaque parameters comprises a ratio or function of volume to surfacearea, heterogeneity index, geometry, and radiodensity of the one or moreregions of plaque within the medical image; generating, by the computersystem, a weighted measure of the determined one or more vascularmorphology parameters and the set of quantified plaque parameters of theone or more regions of plaque; and classifying, by the computer system,the one or more regions of plaque within the medical image as stableplaque or unstable plaque based at least in part on the generatedweighted measure of the determined one or more vascular morphologyparameters and the determined set of quantified plaque parameters,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinone or more of the coronary artery identification algorithm or theplaque identification algorithm comprises an artificial intelligence ormachine learning algorithm.

Embodiment 3: The computer-implemented method of any one of Embodiments1 or 2, wherein the plaque identification algorithm is configured todetermine the one or more regions of plaque by determining a vessel walland lumen wall of the one or more coronary arteries and determining avolume between the vessel wall and lumen wall as the one or more regionsof plaque.

Embodiment 4: The computer-implemented method of any one of Embodiments1-3, wherein the one or more coronary arteries are identified by size.

Embodiment 5: The computer-implemented method of any one of Embodiments1-4, wherein a ratio of volume to surface area of the one or moreregions of plaque below a predetermined threshold is indicative ofstable plaque.

Embodiment 6: The computer-implemented method of any one of Embodiments1-5, wherein a radiodensity of the one or more regions of plaque above apredetermined threshold is indicative of stable plaque.

Embodiment 7: The computer-implemented method of any one of Embodiments1-6, wherein a heterogeneity of the one or more regions of plaque belowa predetermined threshold is indicative of stable plaque.

Embodiment 8: The computer-implemented method of any one of Embodiments1-7, wherein the set of quantified plaque parameters further comprisesdiffusivity of the one or more regions of plaque.

Embodiment 9: The computer-implemented method of any one of Embodiments1-8, wherein the set of quantified plaque parameters further comprises aratio of radiodensity to volume of the one or more regions of plaque.

Embodiment 10: The computer-implemented method of any one of Embodiments1-9, further comprising generating, by the computer system, a proposedtreatment for the subject based at least in part on the classified oneor more regions of plaque.

Embodiment 11: The computer-implemented method of any one of Embodiments1-10, further comprising generating, by the computer system, anassessment of the subject for one or more of atherosclerosis, stenosis,or ischemia based at least in part on the classified one or more regionsof plaque.

Embodiment 12: The computer-implemented method of any one of Embodiments1-11, wherein the medical image comprises a Computed Tomography (CT)image.

Embodiment 13: The computer-implemented method of Embodiment 12, whereinthe medical image comprises a non-contrast CT image.

Embodiment 14: The computer-implemented method of Embodiment 12, whereinthe medical image comprises a contrast-enhanced CT image.

Embodiment 15: The computer-implemented method of any one of Embodiments1-11, wherein the medical image comprises a Magnetic Resonance (MR)image.

Embodiment 16: The computer-implemented method of any one of Embodiments1-11, wherein the medical image is obtained using an imaging techniquecomprising one or more of CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), MR imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 17: The computer-implemented method of any one of Embodiments1-16, wherein the heterogeneity index of one or more regions of plaqueis determined by generating a three-dimensional histogram ofradiodensity values across a geometric shape of the one or more regionsof plaque.

Embodiment 18: The computer-implemented method of any one of Embodiments1-17, wherein the heterogeneity index of one or more regions of plaqueis determined by generating spatial mapping of radiodensity valuesacross the one or more regions of plaque.

Embodiment 19: The computer-implemented method of any one of Embodiments1-18, wherein the set of quantified plaque parameters comprises apercentage composition of plaque comprising different radiodensityvalues.

Embodiment 20: The computer-implemented method of any one of Embodiments1-19, wherein the set of quantified plaque parameters comprises apercentage composition of plaque comprising different radiodensityvalues as a function of volume of plaque.

Embodiment 21: The computer-implemented method of any one of Embodiments1-20, wherein the geometry of the one or more regions of plaquecomprises a round or oblong shape.

Embodiment 22: The computer-implemented method of any one of Embodiments1-21, wherein the one or more vascular morphology parameters comprises aclassification of arterial remodeling.

Embodiment 23: The computer-implemented method of Embodiment 22, whereinthe classification of arterial remodeling comprises positive arterialremodeling, negative arterial remodeling, and intermediate arterialremodeling.

Embodiment 24: The computer-implemented method of Embodiment 22, whereinthe classification of arterial remodeling is determined based at leastin part on a ratio of a largest vessel diameter at the one or moreregions of plaque to a normal reference vessel diameter.

Embodiment 25: The computer-implemented method of Embodiment 23, whereinthe classification of arterial remodeling comprises positive arterialremodeling, negative arterial remodeling, and intermediate arterialremodeling, and wherein positive arterial remodeling is determined whenthe ratio of the largest vessel diameter at the one or more regions ofplaque to the normal reference vessel diameter is more than 1.1, whereinnegative arterial remodeling is determined when the ratio of the largestvessel diameter at the one or more regions of plaque to the normalreference vessel diameter is less than 0.95, and wherein intermediatearterial remodeling is determined when the ratio of the largest vesseldiameter at the one or more regions of plaque to the normal referencevessel diameter is between 0.95 and 1.1.

Embodiment 26: The computer-implemented method of any one of Embodiments1-25, wherein the function of volume to surface area of the one or moreregions of plaque comprises one or more of a thickness or diameter ofthe one or more regions of plaque.

Embodiment 27: The computer-implemented method of any one of Embodiments1-26, wherein the weighted measure is generated by weighting the one ormore vascular morphology parameters and the set of quantified plaqueparameters of the one or more regions of plaque equally.

Embodiment 28: The computer-implemented method of any one of Embodiments1-26, wherein the weighted measure is generated by weighting the one ormore vascular morphology parameters and the set of quantified plaqueparameters of the one or more regions of plaque differently.

Embodiment 29: The computer-implemented method of any one of Embodiments1-26, wherein the weighted measure is generated by weighting the one ormore vascular morphology parameters and the set of quantified plaqueparameters of the one or more regions of plaque logarithmically,algebraically, or utilizing another mathematical transform.

Embodiment 30: A computer-implemented method of quantifying andclassifying vascular plaque based on non-invasive medical imageanalysis, the method comprising: accessing, by a computer system, amedical image of a subject, wherein the medical image of the subject isobtained non-invasively; identifying, by the computer system utilizingan artery identification algorithm, one or more arteries within themedical image of the subject, wherein the artery identificationalgorithm is configured to utilize raw medical images as input;identifying, by the computer system utilizing a plaque identificationalgorithm, one or more regions of plaque within the one or more arteriesidentified from the medical image of the subject, wherein the plaqueidentification algorithm is configured to utilize raw medical images asinput; determining, by the computer system, one or more vascularmorphology parameters and a set of quantified plaque parameters of theone or more identified regions of plaque from the medical image of thesubject, wherein the set of quantified plaque parameters comprises aratio or function of volume to surface area, heterogeneity index,geometry, and radiodensity of the one or more regions of plaque from themedical image; generating, by the computer system, a weighted measure ofthe determined one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaque; andclassifying, by the computer system, the one or more regions of plaquewithin the medical image as stable plaque or unstable plaque based atleast in part on the generated weighted measure of the determined one ormore vascular morphology and the determined set of quantified plaqueparameters, wherein the computer system comprises a computer processorand an electronic storage medium.

Embodiment 31: The computer-implemented method of Embodiment 30, whereinthe identified one or more arteries comprise one or more of carotidarteries, aorta, renal artery, lower extremity artery, or cerebralartery.

Embodiment 32: A computer-implemented method of determiningnon-calcified plaque from a non-contrast Computed Tomography (CT) image,the method comprising: accessing, by a computer system, a non-contrastCT image of a coronary region of a subject; identifying, by the computersystem, epicardial fat on the non-contrast CT image; segmenting, by thecomputer system, arteries on the non-contrast CT image using theidentified epicardial fat as outer boundaries of the arteries;identifying, by the computer system, a first set of pixels within thearteries on the non-contrast CT image comprising a Hounsfield unitradiodensity value below a predetermined radiodensity threshold;classifying, by the computer system, the first set of pixels as a firstsubset of non-calcified plaque; identifying, by the computer system, asecond set of pixels within the arteries on the non-contrast CT imagecomprising a Hounsfield unit radiodensity value within a predeterminedradiodensity range; determining, by the computer system, a heterogeneityindex of the second set of pixels and identifying a subset of the secondset of pixels comprising a heterogeneity index above a heterogeneityindex threshold; classifying, by the computer system, the subset of thesecond set of pixels as a second subset of non-calcified plaque; anddetermining, by the computer system, non-calcified plaque from thenon-contrast CT image by combining the first subset of non-calcifiedplaque and the second subset of non-calcified plaque, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 33: The computer-implemented method of Embodiment 32, whereinthe predetermined radiodensity threshold comprises a Hounsfield unitradiodensity value of 30.

Embodiment 34: The computer-implemented method of any one of Embodiments32-33, wherein the predetermined radiodensity range comprises Hounsfieldunit radiodensity values between 30 and 100.

Embodiment 35: The computer-implemented method of any one of Embodiments32-34, wherein identifying epicardial fat on the non-contrast CT imagefurther comprises: determining a Hounsfield unit radiodensity value ofeach pixel within the non-contrast CT image; and classifying asepicardial fat pixels within the non-contrast CT image with a Hounsfieldunit radiodensity value within a predetermined epicardial fatradiodensity range, wherein the predetermined epicardial fatradiodensity range comprises a Hounsfield unit radiodensity value of−100.

Embodiment 36: The computer-implemented method of any one of Embodiments32-35, wherein the heterogeneity index of the second set of pixels isdetermined by generating spatial mapping of radiodensity values of thesecond set of pixels.

Embodiment 37: The computer-implemented method of any one of Embodiments32-36, wherein the heterogeneity index of the second set of pixels isdetermined by generating a three-dimensional histogram of radiodensityvalues across a geometric region within the second set of pixels.

Embodiment 38: The computer-implemented method of any one of Embodiments32-37, further comprising classifying, by the computer system, a subsetof the second set of pixels comprising a heterogeneity index below theheterogeneity index threshold as blood.

Embodiment 39: The computer-implemented method of any one of Embodiments32-38, further comprising generating a quantized color map of thecoronary region of the subject by assigning a first color to theidentified epicardial fat, assigning a second color to the segmentedarteries, and assigning a third color to the determined non-calcifiedplaque.

Embodiment 40: The computer-implemented method of any one of Embodiments32-39, further comprising: identifying, by the computer system, a thirdset of pixels within the arteries on the non-contrast CT imagecomprising a Hounsfield unit radiodensity value above a predeterminedcalcified radiodensity threshold; and classifying, by the computersystem, the third set of pixels as calcified plaque.

Embodiment 41: The computer-implemented method of any one of Embodiments32-40, further comprising determining, by the computer system, aproposed treatment based at least in part on the determinednon-calcified plaque.

Embodiment 42: A computer-implemented method of determininglow-attenuated plaque from a medical image of a subject, the methodcomprising: accessing, by a computer system, a medical image of asubject; identifying, by the computer system, epicardial fat on themedical image of the subject by: determining a radiodensity value ofeach pixel within the medical image of the subject; and classifying asepicardial fat pixels within the medical image of the subject with aradiodensity value within a predetermined epicardial fat radiodensityrange; segmenting, by the computer system, arteries on the medical imageof the subject using the identified epicardial fat as outer boundariesof the arteries; identifying, by the computer system, a first set ofpixels within the arteries on the medical image of the subjectcomprising a radiodensity value below a predetermined radiodensitythreshold; classifying, by the computer system, the first set of pixelsas a first subset of low-attenuated plaque; identifying, by the computersystem, a second set of pixels within the arteries on the non-contrastCT image comprising a radiodensity value within a predeterminedradiodensity range; determining, by the computer system, a heterogeneityindex of the second set of pixels and identifying a subset of the secondset of pixels comprising a heterogeneity index above a heterogeneityindex threshold; classifying, by the computer system, the subset of thesecond set of pixels as a second subset of low-attenuated plaque; anddetermining, by the computer system, low-attenuated plaque from themedical image of the subject by combining the first subset oflow-attenuated plaque and the second subset of low-attenuated plaque,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 43: The computer-implemented method of Embodiment 42, whereinthe medical image comprises a Computed Tomography (CT) image.

Embodiment 44: The computer-implemented method of Embodiment 42, whereinthe medical image comprises a Magnetic Resonance (MR) image.

Embodiment 45: The computer-implemented method of Embodiment 42, whereinthe medical image comprises an ultrasound image.

Embodiment 46: The computer-implemented method of any one of Embodiments42-45, wherein the medical image comprises an image of a coronary regionof the subject.

Embodiment 47: The computer-implemented method of any one of Embodiments42-46, further comprising determining, by the computer system, aproposed treatment for a disease based at least in part on thedetermined low-attenuated plaque.

Embodiment 48: The computer-implemented method of Embodiment 47, whereinthe disease comprises one or more of arterial disease, renal arterydisease, abdominal atherosclerosis, or carotid atherosclerosis.

Embodiment 49: The computer-implemented method of any one of Embodiments42-48, wherein the heterogeneity index of the second set of pixels isdetermined by generating spatial mapping of radiodensity values of thesecond set of pixels.

Embodiment 50: A computer-implemented method of determiningnon-calcified plaque from a Dual-Energy Computed Tomography (DECT) imageor spectral Computed Tomography (CT) image, the method comprising:accessing, by a computer system, a DECT or spectral CT image of acoronary region of a subject; identifying, by the computer system,epicardial fat on the DECT image or spectral CT; segmenting, by thecomputer system, arteries on the DECT image or spectral CT; identifying,by the computer system, a first set of pixels within the arteries on theDECT or spectral CT image comprising a Hounsfield unit radiodensityvalue below a predetermined radiodensity threshold; classifying, by thecomputer system, the first set of pixels as a first subset ofnon-calcified plaque; identifying, by the computer system, a second setof pixels within the arteries on the DECT or spectral CT imagecomprising a Hounsfield unit radiodensity value within a predeterminedradiodensity range; classifying, by the computer system, a subset of thesecond set of pixels as a second subset of non-calcified plaque; anddetermining, by the computer system, non-calcified plaque from the DECTimage or spectral CT by combining the first subset of non-calcifiedplaque and the second subset of non-calcified plaque, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 51: The computer-implemented method of Embodiment 50, whereinthe subset of the second set of pixels is identified by determining, bythe computer system, a heterogeneity index of the second set of pixelsand identifying the subset of the second set of pixels comprising aheterogeneity index above a heterogeneity index threshold.

Embodiment 52: A computer-implemented method of assessing risk of acardiovascular event for a subject based on non-invasive medical imageanalysis, the method comprising: accessing, by a computer system, amedical image of a coronary region of a subject, wherein the medicalimage of the coronary region of the subject is obtained non-invasively;identifying, by the computer system utilizing a coronary arteryidentification algorithm, one or more coronary arteries within themedical image of the coronary region of the subject, wherein thecoronary artery identification algorithm is configured to utilize rawmedical images as input; identifying, by the computer system utilizing aplaque identification algorithm, one or more regions of plaque withinthe one or more coronary arteries identified from the medical image ofthe coronary region of the subject, wherein the plaque identificationalgorithm is configured to utilize raw medical images as input;determining, by the computer system, one or more vascular morphologyparameters and a set of quantified plaque parameters of the one or moreidentified regions of plaque from the medical image of the coronaryregion of the subject, wherein the set of quantified plaque parameterscomprises a ratio or function of volume to surface area, heterogeneityindex, geometry, and radiodensity of the one or more regions of plaquewithin the medical image; generating, by the computer system, a weightedmeasure of the determined one or more vascular morphology parameters andthe set of quantified plaque parameters of the one or more regions ofplaque; classifying, by the computer system, the one or more regions ofplaque within the medical image as stable plaque or unstable plaquebased at least in part on the generated weighted measure of thedetermined one or more vascular morphology parameters and the determinedset of quantified plaque parameters; generating, by the computer system,a risk of cardiovascular event for the subject based at least in part onthe one or more regions of plaque classified as stable plaque orunstable plaque; accessing, by the computer system, a coronary valuesdatabase comprising one or more known datasets of coronary valuesderived from one or more other subjects and comparing the one or moreregions of plaque classified as stable plaque or unstable plaque to theone or more known datasets of coronary values; updating, by the computersystem, the generated risk of cardiovascular event for the subject basedat least in part on the comparison of the one or more regions of plaqueclassified as stable plaque or unstable plaque to the one or more knowndatasets of coronary values; and generating, by the computer system, aproposed treatment for the subject based at least in part on thecomparison of the one or more regions of plaque classified as stableplaque or unstable plaque to the one or more known datasets of coronaryvalues, wherein the computer system comprises a computer processor andan electronic storage medium.

Embodiment 53: The computer-implemented method of Embodiment 52, whereinthe cardiovascular event comprises one or more of a Major AdverseCardiovascular Event (MACE), rapid plaque progression, or non-responseto medication.

Embodiment 54: The computer-implemented method of any one of Embodiments52-53, wherein the one or more known datasets of coronary valuescomprises one or more parameters of stable plaque and unstable plaquederived from medical images of healthy subjects.

Embodiment 55: The computer-implemented method of any one of Embodiments52-54, wherein the one or more other subjects are healthy.

Embodiment 56: The computer-implemented method of any one of Embodiments52-55, wherein the one or more other subjects have a heightened risk ofa cardiovascular event.

Embodiment 57: The computer-implemented method of any one of Embodiments52-57, further comprising: identifying, by the computer system, one ormore additional cardiovascular structures within the medical image,wherein the one or more additional cardiovascular structures compriseone or more of the left ventricle, right ventricle, left atrium, rightatrium, aortic valve, mitral valve, tricuspid valve, pulmonic valve,aorta, pulmonary artery, inferior and superior vena cava, epicardialfat, or pericardium; determining, by the computer system, one or moreparameters associated with the identified one or more additionalcardiovascular structures; classifying, by the computer system, the oneor more additional cardiovascular structures based at least in part onthe determined one or more parameters; accessing, by the computersystem, a cardiovascular structures values database comprising one ormore known datasets of cardiovascular structures parameters derived frommedical images of one or more other subjects and comparing theclassified one or more additional cardiovascular structures to the oneor more known datasets of cardiovascular structures parameters; andupdating, by the computer system, the generated risk of cardiovascularevent for the subject based at least in part on the comparison of theclassified one or more additional cardiovascular structures to the oneor more known datasets of cardiovascular structures parameters.

Embodiment 58: The computer-implemented method of Embodiment 57, whereinthe one or more additional cardiovascular structures are classified asnormal or abnormal.

Embodiment 59: The computer-implemented method of Embodiment 57, whereinthe one or more additional cardiovascular structures are classified asincreased or decreased.

Embodiment 60: The computer-implemented method of Embodiment 57, whereinthe one or more additional cardiovascular structures are classified asstatic or dynamic over time.

Embodiment 61: The computer-implemented method of any one of Embodiments57-60, further comprising generating, by the computer system, aquantized color map for the additional cardiovascular structures.

Embodiment 62: The computer-implemented method of any one of Embodiments57-61, further comprising updating, by the computer system, the proposedtreatment for the subject based at least in part on the comparison ofthe classified one or more additional cardiovascular structures to theone or more known datasets of cardiovascular structures parameters.

Embodiment 63: The computer-implemented method of any one of Embodiments57-62, further comprising: identifying, by the computer system, one ormore non-cardiovascular structures within the medical image, wherein theone or more non-cardiovascular structures comprise one or more of thelungs, bones, or liver; determining, by the computer system, one or moreparameters associated with the identified one or more non-cardiovascularstructures; classifying, by the computer system, the one or morenon-cardiovascular structures based at least in part on the determinedone or more parameters; accessing, by the computer system, anon-cardiovascular structures values database comprising one or moreknown datasets of non-cardiovascular structures parameters derived frommedical images of one or more other subjects and comparing theclassified one or more non-cardiovascular structures to the one or moreknown datasets of non-cardiovascular structures parameters; andupdating, by the computer system, the generated risk of cardiovascularevent for the subject based at least in part on the comparison of theclassified one or more non-cardiovascular structures to the one or moreknown datasets of non-cardiovascular structures parameters.

Embodiment 64: The computer-implemented method of Embodiment 63, whereinthe one or more non-cardiovascular structures are classified as normalor abnormal.

Embodiment 65: The computer-implemented method of Embodiment 63, whereinthe one or more non-cardiovascular structures are classified asincreased or decreased.

Embodiment 66: The computer-implemented method of Embodiment 63, whereinthe one or more non-cardiovascular structures are classified as staticor dynamic over time.

Embodiment 67: The computer-implemented method of any one of Embodiments63-66, further comprising generating, by the computer system, aquantized color map for the non-cardiovascular structures.

Embodiment 68: The computer-implemented method of any one of Embodiments63-67, further comprising updating, by the computer system, the proposedtreatment for the subject based at least in part on the comparison ofthe classified one or more non-cardiovascular structures to the one ormore known datasets of non-cardiovascular structures parameters.

Embodiment 69: The computer-implemented method of any one of EmbodimentsClaim 63-68, wherein the one or more parameters associated with theidentified one or more non-cardiovascular structures comprises one ormore of ratio of volume to surface area, heterogeneity, radiodensity, orgeometry of the identified one or more non-cardiovascular structures.

Embodiment 70: The computer-implemented method of any one of Embodiments52-69, wherein the medical image comprises a Computed Tomography (CT)image.

Embodiment 71: The computer-implemented method of any one of Embodiments52-69, wherein the medical image comprises a Magnetic Resonance (MR)image.

Embodiment 72: A computer-implemented method of quantifying andclassifying coronary atherosclerosis within a coronary region of asubject based on non-invasive medical image analysis, the methodcomprising: accessing, by a computer system, a medical image of acoronary region of a subject, wherein the medical image of the coronaryregion of the subject is obtained non-invasively; identifying, by thecomputer system utilizing a coronary artery identification algorithm,one or more coronary arteries within the medical image of the coronaryregion of the subject, wherein the coronary artery identificationalgorithm is configured to utilize raw medical images as input;identifying, by the computer system utilizing a plaque identificationalgorithm, one or more regions of plaque within the one or more coronaryarteries identified from the medical image of the coronary region of thesubject, wherein the plaque identification algorithm is configured toutilize raw medical images as input; determining, by the computersystem, one or more vascular morphology parameters and a set ofquantified plaque parameters of the one or more identified regions ofplaque from the medical image of the coronary region of the subject,wherein the set of quantified plaque parameters comprises a ratio orfunction of volume to surface area, heterogeneity index, geometry, andradiodensity of the one or more regions of plaque within the medicalimage; generating, by the computer system, a weighted measure of thedetermined one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaque;quantifying, by the computer system, coronary atherosclerosis of thesubject based at least in part on the set of generated weighted measureof the determined one or more vascular morphology parameters and thedetermined quantified plaque parameters; and classifying, by thecomputer system, coronary atherosclerosis of the subject as one or moreof high risk, medium risk, or low risk based at least in part on thequantified coronary atherosclerosis of the subject, wherein the computersystem comprises a computer processor and an electronic storage medium.

Embodiment 73: The computer-implemented method of Embodiment 72, whereinone or more of the coronary artery identification algorithm or theplaque identification algorithm comprises an artificial intelligence ormachine learning algorithm.

Embodiment 74: The computer-implemented method of any one of Embodiments72 or 73, further comprising determining a numerical calculation ofcoronary stenosis of the subject based at least in part on the one ormore vascular morphology parameters and/or set of quantified plaqueparameters determined from the medical image of the coronary region ofthe subject.

Embodiment 75: The computer-implemented method of any one of Embodiments72-74, further comprising assessing a risk of ischemia for the subjectbased at least in part on the one or more vascular morphology parametersand/or set of quantified plaque parameters determined from the medicalimage of the coronary region of the subject.

Embodiment 76: The computer-implemented method of any one of Embodiments72-75, wherein the plaque identification algorithm is configured todetermine the one or more regions of plaque by determining a vessel walland lumen wall of the one or more coronary arteries and determining avolume between the vessel wall and lumen wall as the one or more regionsof plaque.

Embodiment 77: The computer-implemented method of any one of Embodiments72-76, wherein the one or more coronary arteries are identified by size.

Embodiment 78: The computer-implemented method of any one of Embodiments72-77, wherein a ratio of volume to surface area of the one or moreregions of plaque below a predetermined threshold is indicative of lowrisk.

Embodiment 79: The computer-implemented method of any one of Embodiments72-78, wherein a radiodensity of the one or more regions of plaque abovea predetermined threshold is indicative of low risk.

Embodiment 80: The computer-implemented method of any one of Embodiments72-79, wherein a heterogeneity of the one or more regions of plaquebelow a predetermined threshold is indicative of low risk.

Embodiment 81: The computer-implemented method of any one of Embodiments72-80, wherein the set of quantified plaque parameters further comprisesdiffusivity of the one or more regions of plaque.

Embodiment 82: The computer-implemented method of any one of Embodiments72-81, wherein the set of quantified plaque parameters further comprisesa ratio of radiodensity to volume of the one or more regions of plaque.

Embodiment 83: The computer-implemented method of any one of Embodiments72-82, further comprising generating, by the computer system, a proposedtreatment for the subject based at least in part on the classifiedatherosclerosis.

Embodiment 84: The computer-implemented method of any one of Embodiments72-83, wherein the coronary atherosclerosis of the subject is classifiedby the computer system using a coronary atherosclerosis classificationalgorithm, wherein the coronary atherosclerosis classification algorithmis configured to utilize a combination of the ratio of volume of surfacearea, volume, heterogeneity index, and radiodensity of the one or moreregions of plaque as input.

Embodiment 85: The computer-implemented method of any one of Embodiments72-84, wherein the medical image comprises a Computed Tomography (CT)image.

Embodiment 86: The computer-implemented method of Embodiment 85, whereinthe medical image comprises a non-contrast CT image.

Embodiment 87: The computer-implemented method of Embodiment 85, whereinthe medical image comprises a contrast CT image.

Embodiment 88: The computer-implemented method of any one of Embodiments72-84, wherein the medical image is obtained using an imaging techniquecomprising one or more of CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), MR imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 89: The computer-implemented method of any one of Embodiments72-88, wherein the heterogeneity index of one or more regions of plaqueis determined by generating a three-dimensional histogram ofradiodensity values across a geometric shape of the one or more regionsof plaque.

Embodiment 90: The computer-implemented method of any one of Embodiments72-89, wherein the heterogeneity index of one or more regions of plaqueis determined by generating spatial mapping of radiodensity valuesacross the one or more regions of plaque.

Embodiment 91: The computer-implemented method of any one of Embodiments72-90, wherein the set of quantified plaque parameters comprises apercentage composition of plaque comprising different radiodensityvalues.

Embodiment 92: The computer-implemented method of any one of Embodiments72-91, wherein the set of quantified plaque parameters comprises apercentage composition of plaque comprising different radiodensityvalues as a function of volume of plaque.

Embodiment 93: The computer-implemented method of any one of Embodiments72-92, wherein the weighted measure of the determined one or morevascular morphology parameters and the set of quantified plaqueparameters of the one or more regions of plaque is generated based atleast in part by comparing the determined set of quantified plaqueparameters to one or more predetermined sets of quantified plaqueparameters.

Embodiment 94: The computer-implemented method of Embodiment 93, whereinthe one or more predetermined sets of quantified plaque parameters arederived from one or more medical images of other subjects.

Embodiment 95: The computer-implemented method of Embodiment 93, whereinthe one or more predetermined sets of quantified plaque parameters arederived from one or more medical images of the subject.

Embodiment 96: The computer-implemented method of any one of Embodiments72-95, wherein the geometry of the one or more regions of plaquecomprises a round or oblong shape.

Embodiment 97: The computer-implemented method of any one of Embodiments72-96, wherein the one or more vascular morphology parameters comprisesa classification of arterial remodeling.

Embodiment 98: The computer-implemented method of Embodiment 97, whereinthe classification of arterial remodeling comprises positive arterialremodeling, negative arterial remodeling, and intermediate arterialremodeling.

Embodiment 99: The computer-implemented method of Embodiment 97, whereinthe classification of arterial remodeling is determined based at leastin part on a ratio of a largest vessel diameter at the one or moreregions of plaque to a normal reference vessel diameter.

Embodiment 100: The computer-implemented method of Embodiment 99,wherein the classification of arterial remodeling comprises positivearterial remodeling, negative arterial remodeling, and intermediatearterial remodeling, and wherein positive arterial remodeling isdetermined when the ratio of the largest vessel diameter at the one ormore regions of plaque to the normal reference vessel diameter is morethan 1.1, wherein negative arterial remodeling is determined when theratio of the largest vessel diameter at the one or more regions ofplaque to the normal reference vessel diameter is less than 0.95, andwherein intermediate arterial remodeling is determined when the ratio ofthe largest vessel diameter at the one or more regions of plaque to thenormal reference vessel diameter is between 0.95 and 1.1.

Embodiment 101: The computer-implemented method of any one ofEmbodiments 72-100, wherein the function of volume to surface area ofthe one or more regions of plaque comprises one or more of a thicknessor diameter of the one or more regions of plaque.

Embodiment 102: The computer-implemented method of any one ofEmbodiments 72-101, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaqueequally.

Embodiment 103: The computer-implemented method of any one ofEmbodiments 72-101, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaquedifferently.

Embodiment 104: The computer-implemented method of any one ofEmbodiments 72-101, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters and the set ofquantified plaque parameters of the one or more regions of plaquelogarithmically, algebraically, or utilizing another mathematicaltransform.

Embodiment 105: A computer-implemented method of quantifying a state ofcoronary artery disease based on quantification of plaque, ischemia, andfat inflammation based on non-invasive medical image analysis, themethod comprising: accessing, by a computer system, a medical image of acoronary region of a subject, wherein the medical image of the coronaryregion of the subject is obtained non-invasively; identifying, by thecomputer system utilizing a coronary artery identification algorithm,one or more coronary arteries within the medical image of the coronaryregion of the subject, wherein the coronary artery identificationalgorithm is configured to utilize raw medical images as input;identifying, by the computer system utilizing a plaque identificationalgorithm, one or more regions of plaque within the one or more coronaryarteries identified from the medical image of the coronary region of thesubject, wherein the plaque identification algorithm is configured toutilize raw medical images as input; identifying, by the computer systemutilizing a fat identification algorithm, one or more regions of fatwithin the medical image of the coronary region of the subject, whereinthe fat identification algorithm is configured to utilize raw medicalimages as input; determining, by the computer system, one or morevascular morphology parameters and a set of quantified plaque parametersof the one or more identified regions of plaque from the medical imageof the coronary region of the subject, wherein the set of quantifiedplaque parameters comprises a ratio or function of volume to surfacearea, heterogeneity index, geometry, and radiodensity of the one or moreregions of plaque within the medical image; quantifying, by the computersystem, coronary stenosis based at least in part on the set ofquantified plaque parameters determined from the medical image of thecoronary region of the subject; and determining, by the computer system,a presence or risk of ischemia based at least in part on the set ofquantified plaque parameters determined from the medical image of thecoronary region of the subject; determining, by the computer system, aset of quantified fat parameters of the one or more identified regionsof fat within the medical image of the coronary region of the subject,wherein the set of quantified fat parameters comprises volume, geometry,and radiodensity of the one or more regions of fat within the medicalimage; generating, by the computer system, a weighted measure of thedetermined one or more vascular morphology parameters, the set ofquantified plaque parameters of the one or more regions of plaque, thequantified coronary stenosis, the determined presence or risk ofischemia, and the determined set of quantified fat parameters; andgenerating, by the computer system, a risk assessment of coronarydisease of the subject based at least in part on the generated weightedmeasure of the determined one or more vascular morphology parameters,the set of quantified plaque parameters of the one or more regions ofplaque, the quantified coronary stenosis, the determined presence orrisk of ischemia, and the determined set of quantified fat parameters,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 106: The computer-implemented method of Embodiment 105,wherein one or more of the coronary artery identification algorithm,plaque identification algorithm, or fat identification algorithmcomprises an artificial intelligence or machine learning algorithm.

Embodiment 107: The computer-implemented method of any one ofEmbodiments 105 or 106, further comprising automatically generating, bythe computer system, a Coronary Artery Disease Reporting & Data System(CAD-RADS) classification score of the subject based at least in part onthe quantified coronary stenosis.

Embodiment 108: The computer-implemented method of any one ofEmbodiments 105-107, further comprising automatically generating, by thecomputer system, a CAD-RADS modifier of the subject based at least inpart on one or more of the determined one or more vascular morphologyparameters, the set of quantified plaque parameters of the one or moreregions of plaque, the quantified coronary stenosis, the determinedpresence or risk of ischemia, and the determined set of quantified fatparameters, wherein the CAD-RADS modifier comprises one or more ofnondiagnostic (N), stent (S), graft (G), or vulnerability (V).

Embodiment 109: The computer-implemented method of any one ofEmbodiments 105-108, wherein the coronary stenosis is quantified on avessel-by-vessel basis.

Embodiment 110: The computer-implemented method of any one ofEmbodiments 105-109, wherein the presence or risk of ischemia isdetermined on a vessel-by-vessel basis.

Embodiment 111: The computer-implemented method of any one ofEmbodiments 105-110, wherein the one or more regions of fat comprisesepicardial fat.

Embodiment 112: The computer-implemented method of any one ofEmbodiments 105-111, further comprising generating, by the computersystem, a proposed treatment for the subject based at least in part onthe generated risk assessment of coronary disease.

Embodiment 113: The computer-implemented method of any one ofEmbodiments 105-112, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 114: The computer-implemented method of Embodiment 113,wherein the medical image comprises a non-contrast CT image.

Embodiment 115: The computer-implemented method of Embodiment 113,wherein the medical image comprises a contrast CT image.

Embodiment 116: The computer-implemented method of any one ofEmbodiments 113-115, wherein the determined set of plaque parameterscomprises one or more of a percentage of higher radiodensity calciumplaque or lower radiodensity calcium plaque within the one or moreregions of plaque, wherein higher radiodensity calcium plaque comprisesa Hounsfield radiodensity unit of above 1000, and wherein lowerradiodensity calcium plaque comprises a Hounsfield radiodensity unit ofbelow 1000.

Embodiment 117: The computer-implemented method of any one ofEmbodiments 105-112, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 118: The computer-implemented method of any one ofEmbodiments 105-112, wherein the medical image comprises an ultrasoundimage.

Embodiment 119: The computer-implemented method of any one ofEmbodiments 105-112, wherein the medical image is obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 120: The computer-implemented method of any one ofEmbodiments 105-119, wherein the heterogeneity index of one or moreregions of plaque is determined by generating a three-dimensionalhistogram of radiodensity values across a geometric shape of the one ormore regions of plaque.

Embodiment 121: The computer-implemented method of any one ofEmbodiments 105-119, wherein the heterogeneity index of one or moreregions of plaque is determined by generating spatial mapping ofradiodensity values across the one or more regions of plaque.

Embodiment 122: The computer-implemented method of any one ofEmbodiments 105-121, wherein the set of quantified plaque parameterscomprises a percentage composition of plaque comprising differentradiodensity values.

Embodiment 123: The computer-implemented method of any one ofEmbodiments 105-122, wherein the set of quantified plaque parametersfurther comprises diffusivity of the one or more regions of plaque.

Embodiment 124: The computer-implemented method of any one ofEmbodiments 105-123, wherein the set of quantified plaque parametersfurther comprises a ratio of radiodensity to volume of the one or moreregions of plaque.

Embodiment 125: The computer-implemented method of any one ofEmbodiments 105-124, wherein the plaque identification algorithm isconfigured to determine the one or more regions of plaque by determininga vessel wall and lumen wall of the one or more coronary arteries anddetermining a volume between the vessel wall and lumen wall as the oneor more regions of plaque.

Embodiment 126: The computer-implemented method of any one ofEmbodiments 105-125, wherein the one or more coronary arteries areidentified by size.

Embodiment 127: The computer-implemented method of any one ofEmbodiments 105-126, wherein the generated risk assessment of coronarydisease of the subject comprises a risk score.

Embodiment 128: The computer-implemented method of any one ofEmbodiments 105-127, wherein the geometry of the one or more regions ofplaque comprises a round or oblong shape.

Embodiment 129: The computer-implemented method of any one ofEmbodiments 105-128, wherein the one or more vascular morphologyparameters comprises a classification of arterial remodeling.

Embodiment 130: The computer-implemented method of Embodiment 129,wherein the classification of arterial remodeling comprises positivearterial remodeling, negative arterial remodeling, and intermediatearterial remodeling.

Embodiment 131: The computer-implemented method of Embodiment 129,wherein the classification of arterial remodeling is determined based atleast in part on a ratio of a largest vessel diameter at the one or moreregions of plaque to a normal reference vessel diameter.

Embodiment 132: The computer-implemented method of Embodiment 131,wherein the classification of arterial remodeling comprises positivearterial remodeling, negative arterial remodeling, and intermediatearterial remodeling, and wherein positive arterial remodeling isdetermined when the ratio of the largest vessel diameter at the one ormore regions of plaque to the normal reference vessel diameter is morethan 1.1, wherein negative arterial remodeling is determined when theratio of the largest vessel diameter at the one or more regions ofplaque to the normal reference vessel diameter is less than 0.95, andwherein intermediate arterial remodeling is determined when the ratio ofthe largest vessel diameter at the one or more regions of plaque to thenormal reference vessel diameter is between 0.95 and 1.1.

Embodiment 133: The computer-implemented method of any of Embodiments105-132, wherein the function of volume to surface area of the one ormore regions of plaque comprises one or more of a thickness or diameterof the one or more regions of plaque.

Embodiment 134: The computer-implemented method of any one ofEmbodiments 105-133, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters, the set ofquantified plaque parameters of the one or more regions of plaque, thequantified coronary stenosis, the determined presence or risk ofischemia, and the determined set of quantified fat parameters equally.

Embodiment 135: The computer-implemented method of any one ofEmbodiments 105-133, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters, the set ofquantified plaque parameters of the one or more regions of plaque, thequantified coronary stenosis, the determined presence or risk ofischemia, and the determined set of quantified fat parametersdifferently.

Embodiment 136: The computer-implemented method of any one ofEmbodiments 105-133, wherein the weighted measure is generated byweighting the one or more vascular morphology parameters, the set ofquantified plaque parameters of the one or more regions of plaque, thequantified coronary stenosis, the determined presence or risk ofischemia, and the determined set of quantified fat parameterslogarithmically, algebraically, or utilizing another mathematicaltransform.

Embodiment 137: A computer-implemented method of tracking a plaque-baseddisease based at least in part on determining a state of plaqueprogression of a subject using non-invasive medical image analysis, themethod comprising: accessing, by a computer system, a first set ofplaque parameters associated with a region of a subject, wherein thefirst set of plaque parameters are derived from a first medical image ofthe subject, wherein the first medical image of the subject is obtainednon-invasively at a first point in time; accessing, by a computersystem, a second medical image of the subject, wherein the secondmedical image of the subject is obtained non-invasively at a secondpoint in time, the second point in time being later than the first pointin time; identifying, by the computer system, one or more regions ofplaque from the second medical image; determining, by the computersystem, a second set of plaque parameters associated with the region ofthe subject by analyzing the second medical image and the identified oneor more regions of plaque from the second medical image; analyzing, bythe computer system, a change in one or more plaque parameters bycomparing one or more of the first set of plaque parameters against oneor more of the second set of plaque parameters; determining, by thecomputer system, a state of plaque progression associated with aplaque-based disease for the subject based at least in part on theanalyzed change in the one or more plaque parameters, wherein thedetermined state of plaque progression comprises one or more of rapidplaque progression, non-rapid calcium dominant mixed response, non-rapidnon-calcium dominant mixed response, or plaque regression; and tracking,by the computer system, progression of the plaque-based disease based atleast in part on the determined state of plaque progression, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 138: The computer-implemented method of Embodiment 137,wherein rapid plaque progression is determined when a percent atheromavolume increase of the subject is more than 1% per year, whereinnon-rapid calcium dominant mixed response is determined when a percentatheroma volume increase of the subject is less than 1% per year andcalcified plaque represents more than 50% of total new plaque formation,wherein non-rapid non-calcium dominant mixed response is determined whena percent atheroma volume increase of the subject is less than 1% peryear and non-calcified plaque represents more than 50% of total newplaque formation, and wherein plaque regression is determined when adecrease in total percent atheroma volume is present.

Embodiment 139: The computer-implemented method of any one ofEmbodiments 137-138, further comprising generating, by the computersystem, a proposed treatment for the subject based at least in part onthe determined state of plaque progression of the plaque-based disease.

Embodiment 140: The computer-implemented method of any one ofEmbodiments 137-139, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 141: The computer-implemented method of Embodiment 140,wherein the medical image comprises a non-contrast CT image.

Embodiment 142: The computer-implemented method of Embodiment 140,wherein the medical image comprises a contrast CT image.

Embodiment 143: The computer-implemented method of any one ofEmbodiments 140-142, wherein the determined state of plaque progressionfurther comprises one or more of a percentage of higher radiodensityplaques or lower radiodensity plaques, wherein higher radiodensityplaques comprise a Hounsfield unit of above 1000, and wherein lowerradiodensity plaques comprise a Hounsfield unit of below 1000.

Embodiment 144: The computer-implemented method of any one ofEmbodiments 137-139, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 145: The computer-implemented method of any one ofEmbodiments 137-139, wherein the medical image comprises an ultrasoundimage.

Embodiment 146: The computer-implemented method of any one ofEmbodiments 137-145, wherein the region of the subject comprises acoronary region of the subject.

Embodiment 147: The computer-implemented method of any one ofEmbodiments 137-145, wherein the region of the subject comprises one ormore of carotid arteries, renal arteries, abdominal aorta, cerebralarteries, lower extremities, or upper extremities.

Embodiment 148: The computer-implemented method of any one ofEmbodiments 137-147, wherein the plaque-based disease comprises one ormore of atherosclerosis, stenosis, or ischemia.

Embodiment 149: The computer-implemented method of any one ofEmbodiments 137-148, further comprising: determining, by the computersystem, a first Coronary Artery Disease Reporting & Data System(CAD-RADS) classification score of the subject based at least in part onthe first set of plaque parameters; determining, by the computer system,a second CAD-RADS classification score of the subject based at least inpart on the second set of plaque parameters; and tracking, by thecomputer system, progression of a CAD-RADS classification score of thesubject based on comparing the first CAD-RADS classification score andthe second CAD-RADS classification score.

Embodiment 150: The computer-implemented method of any one ofEmbodiments 137-149, wherein the plaque-based disease is further trackedby the computer system by analyzing one or more of serum biomarkers,genetics, omics, transcriptomics, microbiomics, or metabolomics.

Embodiment 151: The computer-implemented method of any one ofEmbodiments 137-150, wherein the first set of plaque parameterscomprises one or more of a volume, surface area, geometric shape,location, heterogeneity index, and radiodensity of one or more regionsof plaque within the first medical image.

Embodiment 152: The computer-implemented method of any one ofEmbodiments 137-151, wherein the second set of plaque parameterscomprises one or more of a volume, surface area, geometric shape,location, heterogeneity index, and radiodensity of one or more regionsof plaque within the second medical image.

Embodiment 153: The computer-implemented method of any one ofEmbodiments 137-152, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a ratio of radiodensity tovolume of one or more regions of plaque.

Embodiment 154: The computer-implemented method of any one ofEmbodiments 137-153, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a diffusivity of one or moreregions of plaque.

Embodiment 155: The computer-implemented method of any one ofEmbodiments 137-154, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a volume to surface area ratioof one or more regions of plaque.

Embodiment 156: The computer-implemented method of any one ofEmbodiments 137-155, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a heterogeneity index of one ormore regions of plaque.

Embodiment 157: The computer-implemented method of Embodiment 156,wherein the heterogeneity index of one or more regions of plaque isdetermined by generating a three-dimensional histogram of radiodensityvalues across a geometric shape of the one or more regions of plaque.

Embodiment 158: The computer-implemented method of Embodiment 156,wherein the heterogeneity index of one or more regions of plaque isdetermined by generating spatial mapping of radiodensity values acrossthe one or more regions of plaque.

Embodiment 159: The computer-implemented method of any one ofEmbodiments 137-158, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a percentage composition ofplaque comprising different radiodensity values.

Embodiment 160: The computer-implemented method of any one ofEmbodiments 137-159, wherein the first set of plaque parameters and thesecond set of plaque parameters comprise a percentage composition ofplaque comprising different radiodensity values as a function of volumeof plaque.

Embodiment 161: A computer-implemented method of characterizing a changein coronary calcium score of a subject, the method comprising:accessing, by the computer system, a first coronary calcium score of asubject and a first set of plaque parameters associated with a coronaryregion of a subject, the first coronary calcium score and the first setof parameters obtained at a first point in time, wherein the first setof plaque parameters comprises volume, surface area, geometric shape,location, heterogeneity index, and radiodensity for one or more regionsof plaque within the coronary region of the subject; generating, by thecomputer system, a first weighted measure of the accessed first set ofplaque parameters; accessing, by a computer system, a second coronarycalcium score of the subject and one or more medical images of thecoronary region of the subject, the second coronary calcium score andthe one or more medical images obtained at a second point in time, thesecond point in time being later than the first point in time, whereinthe one or more medical images of the coronary region of the subjectcomprises the one or more regions of plaque; determining, by thecomputer system, a change in coronary calcium score of the subject bycomparing the first coronary calcium score and the second coronarycalcium score; identifying, by the computer system, the one or moreregions of plaque from the one or more medical images; determining, bythe computer system, a second set of plaque parameters associated withthe coronary region of the subject by analyzing the one or more medicalimages, wherein the second set of plaque parameters comprises volume,surface area, geometric shape, location, heterogeneity index, andradiodensity for the one or more regions of plaque; generating, by thecomputer system, a second weighted measure of the determined second setof plaque parameters; analyzing, by the computer system, a change in thefirst weighted measure of the accessed first set of plaque parametersand the second weighted measure of the determined second set of plaqueparameters; and characterizing, by the computer system, the change incoronary calcium score of the subject based at least in part on theidentified one or more regions of plaque and the analyzed change in thefirst weighted measure of the accessed first set of plaque parametersand the second weighted measure of the determined second set of plaqueparameters, wherein the change in coronary in coronary calcium score ischaracterized as positive, neutral, or negative, wherein the computersystem comprises a computer processor and an electronic storage medium.

Embodiment 162: The computer-implemented method of Embodiment 161,wherein radiodensity of the one or more regions of plaque is determinedfrom the one or more medical images by analyzing a Hounsfield unit ofthe identified one or more regions of plaque.

Embodiment 163: The computer-implemented method of any one ofEmbodiments 161-162, further comprising determining a change in ratiobetween volume and radiodensity of the one or more regions of plaquewithin the coronary region of the subject, and wherein the change incoronary calcium score of the subject is further characterized based atleast in part the determined change in ratio between volume andradiodensity of one or more regions of plaque within the coronary regionof the subject.

Embodiment 164: The computer-implemented method of any one ofEmbodiments 161-163, wherein the change in coronary calcium score of thesubject is characterized for each vessel.

Embodiment 165: The computer-implemented method of any one ofEmbodiments 161-164, wherein the change in coronary calcium score of thesubject is characterized for each segment.

Embodiment 166: The computer-implemented method of any one ofEmbodiments 161-165, wherein the change in coronary calcium score of thesubject is characterized for each plaque.

Embodiment 167: The computer-implemented method of any one ofEmbodiments 161-166, wherein the first set of plaque parameters and thesecond set of plaque parameters further comprise a diffusivity of theone or more regions of plaque.

Embodiment 168: The computer-implemented method of any one ofEmbodiments 161-167, wherein the change in coronary calcium score of thesubject is characterized as positive when the radiodensity of the one ormore regions of plaque is increased.

Embodiment 169: The computer-implemented method of any one ofEmbodiments 161-168, wherein the change in coronary calcium score of thesubject is characterized as negative when one or more new regions ofplaque are identified from the one or more medical images.

Embodiment 170: The computer-implemented method of any one ofEmbodiments 161-169, wherein the change in coronary calcium score of thesubject is characterized as positive when a volume to surface area ratioof the one or more regions of plaque is decreased.

Embodiment 171: The computer-implemented method of any one ofEmbodiments 161-170, wherein the heterogeneity index of the one or moreregions of plaque is determined by generating a three-dimensionalhistogram of radiodensity values across a geometric shape of the one ormore regions of plaque.

Embodiment 172: The computer-implemented method of any one ofEmbodiments 161-171, wherein the change in coronary calcium score of thesubject is characterized as positive when the heterogeneity index of theone or more regions of plaque is decreased.

Embodiment 173: The computer-implemented method of any one ofEmbodiments 161-172, wherein the second coronary calcium score of thesubject is determined by analyzing the one or more medical images of thecoronary region of the subject.

Embodiment 174: The computer-implemented method of any one ofEmbodiments 161-172, wherein the second coronary calcium score of thesubject is accessed from a database.

Embodiment 175: The computer-implemented method of any one ofEmbodiments 161-174, wherein the one or more medical images of thecoronary region of the subject comprises an image obtained from anon-contrast Computed Tomography (CT) scan.

Embodiment 176: The computer-implemented method of any one ofEmbodiments 161-174, wherein the one or more medical images of thecoronary region of the subject comprises an image obtained from acontrast-enhanced CT scan.

Embodiment 177: The computer-implemented method of Embodiment 176,wherein the one or more medical images of the coronary region of thesubject comprises an image obtained from a contrast-enhanced CTangiogram.

Embodiment 178: The computer-implemented method of any one ofEmbodiments 161-177, wherein a positive characterization of the changein coronary in coronary calcium score is indicative of plaquestabilization.

Embodiment 179: The computer-implemented method of any one ofEmbodiments 161-178, wherein the first set of plaque parameters and thesecond set of plaque parameters further comprise radiodensity of avolume around plaque

Embodiment 180: The computer-implemented method of any one ofEmbodiments 161-179, wherein the change in coronary calcium score of thesubject is characterized by a machine learning algorithm utilized by thecomputer system.

Embodiment 181: The computer-implemented method of any one ofEmbodiments 161-180, wherein the first weighted measure is generated byweighting the accessed first set of plaque parameters equally.

Embodiment 182: The computer-implemented method of any one ofEmbodiments 161-180, wherein the first weighted measure is generated byweighting the accessed first set of plaque parameters differently.

Embodiment 183: The computer-implemented method of any one ofEmbodiments 161-180, wherein the first weighted measure is generated byweighting the accessed first set of plaque parameters logarithmically,algebraically, or utilizing another mathematical transform.

Embodiment 184: A computer-implemented method of generating prognosis ofa cardiovascular event for a subject based on non-invasive medical imageanalysis, the method comprising: accessing, by a computer system, amedical image of a coronary region of a subject, wherein the medicalimage of the coronary region of the subject is obtained non-invasively;identifying, by the computer system utilizing a coronary arteryidentification algorithm, one or more coronary arteries within themedical image of the coronary region of the subject, wherein thecoronary artery identification algorithm is configured to utilize rawmedical images as input; identifying, by the computer system utilizing aplaque identification algorithm, one or more regions of plaque withinthe one or more coronary arteries identified from the medical image ofthe coronary region of the subject, wherein the plaque identificationalgorithm is configured to utilize raw medical images as input;determining, by the computer system, a set of quantified plaqueparameters of the one or more identified regions of plaque within themedical image of the coronary region of the subject, wherein the set ofquantified plaque parameters comprises volume, surface area, ratio ofvolume to surface area, heterogeneity index, geometry, and radiodensityof the one or more regions of plaque within the medical image;classifying, by the computer system, the one or more regions of plaquewithin the medical image as stable plaque or unstable plaque based atleast in part on the determined set of quantified plaque parameters;determining, by the computer system, a volume of unstable plaqueclassified within the medical image and a total volume of the one ormore coronary arteries within the medical image; determining, by thecomputer system, a ratio of volume of unstable plaque to the totalvolume of the one or more coronary arteries; generating, by the computersystem, a prognosis of a cardiovascular event for the subject based atleast in part on analyzing the ratio of volume of unstable plaque to thetotal volume of the one or more coronary arteries, the volume of the oneor more regions of plaque, and the volume of unstable plaque classifiedwithin the medical image, wherein the analyzing comprises conducting acomparison to a known dataset of one or more ratios of volume ofunstable plaque to total volume of one or more coronary arteries, volumeof one or more regions of plaque, and volume of unstable plaque, whereinthe known dataset is collected from other subjects; and generating, bythe computer system, treatment plan for the subject based at least inpart on the generated prognosis of cardiovascular event for the subject,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 185: The computer-implemented method of Embodiment 184,further comprising generating, by the computer system, a weightedmeasure of the ratio of volume of unstable plaque to the total volume ofthe one or more coronary arteries, the volume of the one or more regionsof plaque, and the volume of unstable plaque classified within themedical image, wherein the prognosis of cardiovascular event is furthergenerated by comparing the weighted measure to one or more weightedmeasures derived from the known dataset.

Embodiment 186: The computer-implemented method of Embodiment 185,wherein the weighted measure is generated by weighting the ratio ofvolume of unstable plaque to the total volume of the one or morecoronary arteries, the volume of the one or more regions of plaque, andthe volume of unstable plaque classified within the medical imageequally.

Embodiment 187: The computer-implemented method of Embodiment 185,wherein the weighted measure is generated by weighting the ratio ofvolume of unstable plaque to the total volume of the one or morecoronary arteries, the volume of the one or more regions of plaque, andthe volume of unstable plaque classified within the medical imagedifferently.

Embodiment 188: The computer-implemented method of Embodiment 185,wherein the weighted measure is generated by weighting the ratio ofvolume of unstable plaque to the total volume of the one or morecoronary arteries, the volume of the one or more regions of plaque, andthe volume of unstable plaque classified within the medical imagelogarithmically, algebraically, or utilizing another mathematicaltransform.

Embodiment 189: The computer-implemented method of any one ofEmbodiments 184-188, further comprising analyzing, by the computersystem, a medical image of a non-coronary cardiovascular system of thesubject, and wherein the prognosis of a cardiovascular event for thesubject is further generated based at least in part on the analyzedmedical image of the non-coronary cardiovascular system of the subject.

Embodiment 190: The computer-implemented method of any one ofEmbodiments 184-189, further comprising accessing, by the computersystem, results of a blood chemistry or biomarker test of the subject,and wherein the prognosis of a cardiovascular event for the subject isfurther generated based at least in part on the results of the bloodchemistry or biomarker test of the subject.

Embodiment 191: The computer-implemented method of any one ofEmbodiments 184-190, wherein the generated prognosis of a cardiovascularevent for the subject comprises a risk score of a cardiovascular eventfor the subject.

Embodiment 192: The computer-implemented method of any one ofEmbodiments 184-191, wherein the prognosis of a cardiovascular event isgenerated by the computer system utilizing an artificial intelligence ormachine learning algorithm.

Embodiment 193: The computer-implemented method of any one ofEmbodiments 184-192, wherein the cardiovascular event comprises one ormore of atherosclerosis, stenosis, or ischemia.

Embodiment 194: The computer-implemented method of any one ofEmbodiments 184-193, wherein the generated treatment plan comprises oneor more of use of statins, lifestyle changes, or surgery.

Embodiment 195: The computer-implemented method of any one ofEmbodiments 184-194, wherein one or more of the coronary arteryidentification algorithm or the plaque identification algorithmcomprises an artificial intelligence or machine learning algorithm.

Embodiment 196: The computer-implemented method of any one ofEmbodiments 184-195, wherein the plaque identification algorithm isconfigured to determine the one or more regions of plaque by determininga vessel wall and lumen wall of the one or more coronary arteries anddetermining a volume between the vessel wall and lumen wall as the oneor more regions of plaque.

Embodiment 197: The computer-implemented method of any one ofEmbodiments 184-196, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 198: The computer-implemented method of Embodiment 197,wherein the medical image comprises a non-contrast CT image.

Embodiment 199: The computer-implemented method of Embodiment 197,wherein the medical image comprises a contrast CT image.

Embodiment 200: The computer-implemented method of any one ofEmbodiments 184-196, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 201: The computer-implemented method of any one ofEmbodiments 184-196, wherein the medical image is obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 202: A computer-implemented method of determiningpatient-specific stent parameters and guidance for implantation based onnon-invasive medical image analysis, the method comprising: accessing,by a computer system, a medical image of a coronary region of a patient,wherein the medical image of the coronary region of the patient isobtained non-invasively; identifying, by the computer system utilizing acoronary artery identification algorithm, one or more coronary arterieswithin the medical image of the coronary region of the patient, whereinthe coronary artery identification algorithm is configured to utilizeraw medical images as input; identifying, by the computer systemutilizing a plaque identification algorithm, one or more regions ofplaque within the one or more coronary arteries identified from themedical image of the coronary region of the patient, wherein the plaqueidentification algorithm is configured to utilize raw medical images asinput; determining, by the computer system, a set of quantified plaqueparameters of the one or more identified regions of plaque from themedical image of the coronary region of the patient, wherein the set ofquantified plaque parameters comprises a ratio or function of volume tosurface area, heterogeneity index, location, geometry, and radiodensityof the one or more regions of plaque within the medical image;determining, by the computer system, a set of stenosis vessel parametersof the one or more coronary arteries within the medical image of thecoronary region of the patient, wherein the set of vessel parameterscomprises volume, curvature, vessel wall, lumen wall, and diameter ofthe one or more coronary arteries within the medical image in thepresence of stenosis; determining, by the computer system, a set ofnormal vessel parameters of the one or more coronary arteries within themedical image of the coronary region of the patient, wherein the set ofvessel parameters comprises volume, curvature, vessel wall, lumen wall,and diameter of the one or more coronary arteries within the medicalimage without stenosis, wherein the set of normal vessel parameters aredetermined by graphically removing from the medical image of thecoronary region of the patient the identified one or more regions ofplaque; determining, by the computer system, a predicted effectivenessof stent implantation for the patient based at least in part on the setof quantified plaque parameters and the set of vessel parameters;generating, by the computer system, patient-specific stent parametersfor the patient when the predicted effectiveness of stent implantationfor the patient is above a predetermined threshold, wherein thepatient-specific stent parameters are generated based at least in parton the set of quantified plaque parameters, the set of vesselparameters, and the set of normal vessel parameters; and generating, bythe computer system, guidance for implantation of a patient-specificstent comprising the patient-specific stent parameters, wherein theguidance for implantation of the patient-specific stent is generatedbased at least in part on the set of quantified plaque parameters andthe set of vessel parameters, wherein the generated guidance forimplantation of the patient-specific stent comprises insertion ofguidance wires and positioning of the patient-specific stent, whereinthe computer system comprises a computer processor and an electronicstorage medium.

Embodiment 203: The computer-implemented method of Embodiment 202,further comprising accessing, by the computer system, apost-implantation medical image of the coronary region of the patientand performing post-implantation analysis.

Embodiment 204: The computer-implemented method of Embodiment 203,further comprising generating, by the computer system, a treatment planfor the patient based at least in part on the post-implantationanalysis.

Embodiment 205: The computer-implemented method of Embodiment 204,wherein the generated treatment plan comprises one or more of use ofstatins, lifestyle changes, or surgery.

Embodiment 206: The computer-implemented method of any one ofEmbodiments 202-205, wherein the set of stenosis vessel parameterscomprises a location, curvature, and diameter of bifurcation of the oneor more coronary arteries.

Embodiment 207: The computer-implemented method of any one ofEmbodiments 202-206, wherein the patient-specific stent parameterscomprise a diameter of the patient-specific stent.

Embodiment 208: The computer-implemented method of Embodiment 207,wherein the diameter of the patient-specific stent is substantiallyequal to the diameter of the one or more coronary arteries withoutstenosis.

Embodiment 209: The computer-implemented method of Embodiment 207,wherein the diameter of the patient-specific stent is less than thediameter of the one or more coronary arteries without stenosis.

Embodiment 210: The computer-implemented method of any one ofEmbodiments 202-209, wherein the predicted effectiveness of stentimplantation for the patient is determined by the computer systemutilizing an artificial intelligence or machine learning algorithm.

Embodiment 211: The computer-implemented method of any one ofEmbodiments 202-210, wherein the patient-specific stent parameters forthe patient are generated by the computer system utilizing an artificialintelligence or machine learning algorithm.

Embodiment 212: The computer-implemented method of any one ofEmbodiments 202-211, wherein one or more of the coronary arteryidentification algorithm or the plaque identification algorithmcomprises an artificial intelligence or machine learning algorithm.

Embodiment 213: The computer-implemented method of any one ofEmbodiments 202-212, wherein the plaque identification algorithm isconfigured to determine the one or more regions of plaque by determininga vessel wall and lumen wall of the one or more coronary arteries anddetermining a volume between the vessel wall and lumen wall as the oneor more regions of plaque.

Embodiment 214: The computer-implemented method of any one ofEmbodiments 202-213, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 215: The computer-implemented method of Embodiment 214,wherein the medical image comprises a non-contrast CT image.

Embodiment 216: The computer-implemented method of Embodiment 214,wherein the medical image comprises a contrast CT image.

Embodiment 217: The computer-implemented method of any one ofEmbodiments 202-213, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 218: The computer-implemented method of any one ofEmbodiments 202-213, wherein the medical image is obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 219: A computer-implemented method of generating apatient-specific report on coronary artery disease for a patient basedon non-invasive medical image analysis, the method comprising:accessing, by a computer system, a medical image of a coronary region ofa patient, wherein the medical image of the coronary region of thepatient is obtained non-invasively; identifying, by the computer systemutilizing a coronary artery identification algorithm, one or morecoronary arteries within the medical image of the coronary region of thepatient, wherein the coronary artery identification algorithm isconfigured to utilize raw medical images as input; identifying, by thecomputer system utilizing a plaque identification algorithm, one or moreregions of plaque within the one or more coronary arteries identifiedfrom the medical image of the coronary region of the patient, whereinthe plaque identification algorithm is configured to utilize raw medicalimages as input; determining, by the computer system, one or morevascular morphology parameters and a set of quantified plaque parametersof the one or more identified regions of plaque from the medical imageof the coronary region of the patient, wherein the set of quantifiedplaque parameters comprises a ratio or function of volume to surfacearea, volume, heterogeneity index, location, geometry, and radiodensityof the one or more regions of plaque within the medical image;quantifying, by the computer system, stenosis and atherosclerosis of thepatient based at least in part on the set of quantified plaqueparameters determined from the medical image; generating, by thecomputer system, one or more annotated medical images based at least inpart on the medical image, the quantified stenosis and atherosclerosisof the patient, and the set of quantified plaque parameters determinedfrom the medical image; determining, by the computer system, a risk ofcoronary artery disease for the patient based at least in part bycomparing the quantified stenosis and atherosclerosis of the patient andthe set of quantified plaque parameters determined from the medicalimage to a known dataset of one or more quantified stenosis andatherosclerosis and one or more quantified plaque parameters derivedfrom one or more medial images of healthy subjects within an age groupof the patient; dynamically generating, by the computer system, apatient-specific report on coronary artery disease for the patient,wherein the generated patient-specific report comprises the one or moreannotated medical images, one or more of the set of quantified plaqueparameters, and determined risk of coronary artery disease, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 220: The computer-implemented method of Embodiment 219,wherein the patient-specific report comprises a cinematic report.

Embodiment 221: The computer-implemented method of Embodiment 220,wherein the patient-specific report comprises content configured toprovide an Augmented Reality (AR) or Virtual Reality (VR) experience.

Embodiment 222: The computer-implemented method of any one ofEmbodiments 219-221, wherein the patient-specific report comprises audiodynamically generated for the patient based at least in part on thequantified stenosis and atherosclerosis of the patient, the set ofquantified plaque parameters determined from the medical image, anddetermined risk of coronary artery disease.

Embodiment 223: The computer-implemented method of any one ofEmbodiments 219-222, wherein the patient-specific report comprisesphrases dynamically generated for the patient based at least in part onthe quantified stenosis and atherosclerosis of the patient, the set ofquantified plaque parameters determined from the medical image, anddetermined risk of coronary artery disease.

Embodiment 224: The computer-implemented method of any one ofEmbodiments 219-223, further comprising generating, by the computersystem, a treatment plan for the patient based at least in part on thequantified stenosis and atherosclerosis of the patient, the set ofquantified plaque parameters determined from the medical image, anddetermined risk of coronary artery disease, wherein the patient-specificreport comprises the generated treatment plan.

Embodiment 225: The computer-implemented method of Embodiment 224,wherein the generated treatment plan comprises one or more of use ofstatins, lifestyle changes, or surgery.

Embodiment 226: The computer-implemented method of any one ofEmbodiments 219-225, further comprising tracking, by the computersystem, progression of coronary artery disease for the patient based atleast in part on comparing one or more of the set of quantified plaqueparameters determined from the medical image against one or moreprevious quantified plaque parameters derived from a previous medicalimage of the patient, wherein the patient-specific report comprises thetracked progression of coronary artery disease.

Embodiment 227: The computer-implemented method of any one ofEmbodiments 219-226, wherein one or more of the coronary arteryidentification algorithm or the plaque identification algorithmcomprises an artificial intelligence or machine learning algorithm.

Embodiment 228: The computer-implemented method of any one ofEmbodiments 219-227, wherein the plaque identification algorithm isconfigured to determine the one or more regions of plaque by determininga vessel wall and lumen wall of the one or more coronary arteries anddetermining a volume between the vessel wall and lumen wall as the oneor more regions of plaque.

Embodiment 229: The computer-implemented method of any one ofEmbodiments 219-228, wherein the medical image comprises a ComputedTomography (CT) image.

Embodiment 230: The computer-implemented method of Embodiment 229,wherein the medical image comprises a non-contrast CT image.

Embodiment 231: The computer-implemented method of Embodiment 229,wherein the medical image comprises a contrast CT image.

Embodiment 232: The computer-implemented method of any one ofEmbodiments 219-228, wherein the medical image comprises a MagneticResonance (MR) image.

Embodiment 233: The computer-implemented method of any one ofEmbodiments 219-228, wherein the medical image is obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 234: A system comprising: at least one non-transitorycomputer storage medium configured to at least store computer-executableinstructions, a set of computed tomography (CT) images of a patient'scoronary vessels, vessel labels, and artery information associated withthe set of CT images including information of stenosis, plaque, andlocations of segments of the coronary vessels; one or more computerhardware processors in communication with the at least onenon-transitory computer storage medium, the one or more computerhardware processors configured to execute the computer-executableinstructions to at least: generate and display a user interface a firstpanel including an artery tree comprising a three-dimensional (3D)representation of coronary vessels depicting coronary vessels identifiedin the CT images, and including segment labels related to the arterytree, the artery tree not including heart tissue between branches of theartery tree; in response to an input on the user interface indicatingthe selection of a coronary vessel in the artery tree in the firstpanel, generate and display on the user interface a second panelillustrating at least a portion of the selected coronary vessel in atleast one straightened multiplanar vessel (SMPR) view; generate anddisplay on the user interface a third panel showing a cross-sectionalview of the selected coronary vessel, the cross-sectional view generatedusing one of the set of CT images of the selected coronary vessel,wherein locations along the at least one SMPR view are each associatedwith one of the CT images in the set of CT images such that a selectionof a particular location along the coronary vessel in the at least oneSMPR view displays the associated CT image in the cross-sectional viewin the third panel; and in response to an input on the third panelindicating a first location along the selected coronary artery in the atleast one SMPR view, display a cross-sectional view associated with theselected coronary artery at the first location in the third panel.

Embodiment 235: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to, in response to an input on thesecond panel pf the user interface indicating a second location alongthe selected coronary artery in the at least one SMPR view, display theassociated CT scan associated with the second location in across-sectional view in the third panel.

Embodiment 236: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to: in response to a second input onthe user interface indicating the selection of a second coronary vesselin the artery tree displayed in the first panel, generate and display inthe second panel least a portion of the selected second coronary vesselin at least one straightened multiplanar vessel (SMPR) view, andgenerate and display on the third panel a cross-sectional view of theselected second coronary vessel, the cross-sectional view generatedusing one of the set of CT images of the selected second coronaryvessel, wherein locations along the selected second coronary artery inthe at least one SMPR view are each associated with one of the CT imagesin the set of CT images such that a selection of a particular locationalong the second coronary vessel in the at least one SMPR view displaysthe associated CT image in the cross-sectional view in the third panel.

Embodiment 237: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to identify thevessel segments using a machine learning algorithm that processes the CTimages prior to storing the artery information on the at least onenon-transitory computer storage medium.

Embodiment 238: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to generate and display on the userinterface in a fourth panel a cartoon artery tree, the cartoon arterytree comprising a non-patient specific graphical representation of acoronary artery tree, and wherein in response to a selection of a vesselsegment in the cartoon artery tree, a view of the selected vesselsegment is displayed in a panel of the user interface in a SMPR view,and upon selection of a location of the vessel segment displayed in theSMPR view, generate and display in the user interface a panel thatdisplays information about the selected vessel at the selected location.

Embodiment 239: The system of embodiment 238, wherein the displayedinformation includes information relating to stenosis and plaque of theselected vessel.

Embodiment 240: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to generate and segment name labels,proximal to a respective segment on the artery tree, indicative of thename of the segment.

Embodiment 241: The system of embodiment 240, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to, in response to an input selectionof a first segment name label displayed on the user interface, generateand display on the user interface a panel having a list of vesselsegment names and indicating the current name of the selected vesselsegment; and in response to an input selection of a second segment namelabel on the list, replace the first segment name label with the secondsegment name label of the displayed artery tree in the user interface.

Embodiment 242: The system of embodiment 234, wherein the at least oneSMPR view of the selected coronary vessel comprises at least two SMPRviews of the selected coronary vessel displayed adjacently at arotational interval.

Embodiment 243: The system of embodiment 234, wherein the at least oneSMPR view include four SMPR views displayed at a relative rotation of0°, 22.5°, 45°, and 67.5°.

Embodiment 244: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to, in response to a user input, rotatethe at least one SMPR view in increments of 1°.

Embodiment 245: The system of embodiment 234, wherein the artery tree,the at least one SMPR view, and the cross-sectional view are displayedconcurrently on the user interface.

Embodiment 246: The system of embodiment 245, wherein the artery tree isdisplayed in a center portion of the user panel, the cross-sectionalview is displayed in a center portion of the user interface above orbelow the artery tree, and the at least one SMPR view are displayed onone side of the center portion of the user interface.

Embodiment 247: The system of embodiment 246, wherein the one or morecomputer hardware processors are further configured to generate anddisplay, on one side of the center portion of the user interface, one ormore anatomical plane views corresponding to the selected coronaryartery, the anatomical plane views of the selected coronary vessel basedon the CT images

Embodiment 248: The system of embodiment 247, wherein the anatomicalplane views comprise three anatomical plane views.

Embodiment 249: The system of embodiment 247, wherein the anatomicalplane views comprise at least one of an axial plane view, a coronalplane view, or a sagittal plane view.

Embodiment 250: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to receive arotation input on the user interface, and rotate the at least one SMPRviews incrementally based on the rotation input.

Embodiment 251: The system of embodiment 234, wherein the at least onenon-transitory computer storage medium is further configured to at leaststore vessel wall information including information indicative of thelumen and the vessel walls of the coronary artery vessels, and whereinthe one or more computer hardware processors are further configured tographically display lumen and vessel wall information corresponding tothe coronary vessel displayed in the cross-sectional view in the thirdpanel.

Embodiment 252: The system of embodiment 251, wherein and one or morecomputer hardware processors are further configured to displayinformation of the lumen and the vessel wall on the user interface basedon the selected portion of the coronary vessel in the at least one SMPRview.

Embodiment 253: The system of embodiment 251, wherein and one or morecomputer hardware processors are further configured to displayinformation of plaque based on the selected portion of the coronaryvessel in the at least one SMPR view.

Embodiment 254: The system of embodiment 251, wherein and one or morecomputer hardware processors are further configured to displayinformation of stenosis based on the selected portion of the coronaryvessel in the at least one SMPR view.

Embodiment 255: The system of embodiment 234, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to generate and display on the userinterface a cartoon artery tree, the cartoon artery tree being anon-patient specific graphical representation of an artery tree, whereinportions of the artery tree are displayed in a color that corresponds toa risk level.

Embodiment 256: The system of embodiment 255, wherein the risk level isbased on stenosis.

Embodiment 257: The system of embodiment 255, wherein the risk level isbased on a plaque.

Embodiment 258: The system of embodiment 255, wherein the risk level isbased on ischemia.

Embodiment 259: The system of embodiment 255, wherein the one or morecomputer hardware processors are further configured to execute thecomputer-executable instructions to, in response to selecting a portionof the cartoon artery tree, displaying on the second panel a SMPR viewof the vessel corresponding to the selected portion of the cartoonartery tree, and displaying on the third panel a cross-sectional view ofcorresponding to the selected portion of the cartoon artery tree.

Embodiment 269: A system comprising: means for storingcomputer-executable instructions, a set of computed tomography (CT)images of a patient's coronary vessels, vessel labels, and arteryinformation associated with the set of CT images including informationof stenosis, plaque, and locations of segments of the coronary vessels;and means for executing the computer-executable instructions to atleast: generate and display a user interface a first panel including anartery tree comprising a three-dimensional (3D) representation ofcoronary vessels based on the CT images and depicting coronary vesselsidentified in the CT images, and depicting segment labels, the arterytree not including heart tissue between branches of the artery tree; inresponse to an input on the user interface indicating the selection of acoronary vessel in the artery tree in the first panel, generate anddisplay on the user interface a second panel illustrating at least aportion of the selected coronary vessel in at least one straightenedmultiplanar vessel (SMPR) view; generate and display on the userinterface a third panel showing a cross-sectional view of the selectedcoronary vessel, the cross-sectional view generated using one of the setof CT images of the selected coronary vessel, wherein locations alongthe at least one SMPR view are each associated with one of the CT imagesin the set of CT images such that a selection of a particular locationalong the coronary vessel in the at least one SMPR view displays theassociated CT image in the cross-sectional view in the third panel; andin response to an input on the user interface indicating a firstlocation along the selected coronary artery in the at least one SMPRview, display the associated CT scan associated with the in thecross-sectional view in the third panel.

Embodiment 261: A method for analyzing CT images and correspondinginformation, the method comprising: storing computer-executableinstructions, a set of computed tomography (CT) images of a patient'scoronary vessels, vessel labels, and artery information associated withthe set of CT images including information of stenosis, plaque, andlocations of segments of the coronary vessels; generating and displayingin a user interface a first panel including an artery tree comprising athree-dimensional (3D) representation of coronary vessels based on theCT images and depicting coronary vessels identified in the CT images,and depicting segment labels, the artery tree not including heart tissuebetween branches of the artery tree; receiving a first input indicatinga selection of a coronary vessel in the artery tree in the first panel;in response to the first input, generating and displaying on the userinterface a second panel illustrating at least a portion of the selectedcoronary vessel in at least one straightened multiplanar vessel (SMPR)view; generating and displaying on the user interface a third panelshowing a cross-sectional view of the selected coronary vessel, thecross-sectional view generated using one of the set of CT images of theselected coronary vessel, wherein locations along the at least one SMPRview are each associated with one of the CT images in the set of CTimages such that a selection of a particular location along the coronaryvessel in the at least one SMPR view displays the associated CT image inthe cross-sectional view in the third panel; receiving a second input onthe user interface indicating a first location along the selectedcoronary artery in the at least one SMPR view; and in response to thesecond input, displaying the associated CT scan associated in thecross-sectional view in the third panel, wherein the method is performedby one or more computer hardware processors executingcomputer-executable instructions in communication stored on one or morenon-transitory computer storage mediums.

Embodiment 262: The method of embodiment 261, further comprising, inresponse to an input on the second panel pf the user interfaceindicating a second location along the selected coronary artery in theat least one SMPR view, display the associated CT scan associated withthe second location in a cross-sectional view in the third panel.

Embodiment 263: The method of any one of embodiments 261 and 262,further comprising: in response to a second input on the user interfaceindicating the selection of a second coronary vessel in the artery treedisplayed in the first panel, generating and displaying in the secondpanel least a portion of the selected second coronary vessel in at leastone straightened multiplanar vessel (SMPR) view, and generating anddisplaying on the third panel a cross-sectional view of the selectedsecond coronary vessel, the cross-sectional view generated using one ofthe set of CT images of the selected second coronary vessel, whereinlocations along the selected second coronary artery in the at least oneSMPR view are each associated with one of the CT images in the set of CTimages such that a selection of a particular location along the secondcoronary vessel in the at least one SMPR view displays the associated CTimage in the cross-sectional view in the third panel.

Embodiment 264: The method of any one embodiments 261-263, furthercomprising generating and displaying on the user interface in a fourthpanel a cartoon artery tree, the cartoon artery tree comprising anon-patient specific graphical representation of a coronary artery tree,and wherein in response to a selection of a vessel segment in thecartoon artery tree, a view of the selected vessel segment is displayedin a panel of the user interface in a SMPR view, and upon selection of alocation of the vessel segment displayed in the SMPR view, generatingand displaying in the user interface a panel that displays informationabout the selected vessel at the selected location.

Embodiment 265: The method of embodiment 264, wherein the displayedinformation includes information relating to stenosis and plaque of theselected vessel.

Embodiment 266: The method of any one of embodiments 261-265, furthercomprising generating and displaying segment name labels, proximal to arespective segment on the artery tree, indicative of the name of thesegment, using the stored artery information.

Embodiment 267: The method of any one of embodiments 261-266, furthercomprising, in response to an input selection of a first segment namelabel displayed on the user interface, generating and displaying on theuser interface a panel having a list of vessel segment names andindicating the current name of the selected vessel segment, and inresponse to an input selection of a second segment name label on thelist, replacing the first segment name label with the second segmentname label of the displayed artery tree in the user interface.

Embodiment 268: The method of any one of embodiments 261-267, furthercomprising generating and displaying a tool bar on a fourth panel of theuser interface, the tool bar comprising tools to add, delete, or reviseartery information displayed on the user interface.

Embodiment 269: The method of embodiment 268, wherein the tools on thetoolbar include a lumen wall tool, a snap to vessel wall tool, a snap tolumen wall tool, vessel wall tool, a segment tool, a stenosis tool, aplaque overlay tool a snap to centerline tool, chronic total occlusiontool, stent tool, an exclude tool, a tracker tool, or a distancemeasurement tool.

Embodiment 270: The method of embodiment 268, wherein the tools on thetoolbar include a lumen wall tool, a snap to vessel wall tool, a snap tolumen wall tool, vessel wall tool, a segment tool, a stenosis tool, aplaque overlay tool a snap to centerline tool, chronic total occlusiontool, stent tool, an exclude tool, a tracker tool, and a distancemeasurement tool.

Embodiment 271: A normalization device configured to facilitatenormalization of medical images of a coronary region of a subject for analgorithm-based medical imaging analysis, the normalization devicecomprising: a substrate having a width, a length, and a depth dimension,the substrate having a proximal surface and a distal surface, theproximal surface adapted to be placed adjacent to a surface of a bodyportion of a patient; a plurality of compartments positioned within thesubstrate, each of the plurality of compartments configured to hold asample of a known material, wherein: a first subset of the plurality ofcompartments hold samples of a contrast material with differentconcentrations, a second subset of the plurality of compartments holdsamples of materials representative of materials to be analyzed by thealgorithm-based medical imaging analysis, and a third subset of theplurality of compartments hold samples of phantom materials.

Embodiment 272: The normalization device of Embodiment 271, wherein thecontrast material comprises one of iodine, Gad, Tantalum, Tungsten,Gold, Bismuth, or Ytterbium.

Embodiment 273: The normalization device of any of Embodiments 271-272,wherein the samples of materials representative of materials to beanalyzed by the algorithm-based medical imaging analysis comprise atleast two of calcium 1000 HU, calcium 220 HU, calcium 150 HU, calcium130 HU, and a low attenuation (e.g., 30 HU) material.

Embodiment 274: The normalization device of any of Embodiments 271-273,wherein the samples of phantom materials comprise one more of water,fat, calcium, uric acid, air, iron, or blood.

Embodiment 275: The normalization device of any of Embodiments 271-274,further comprising one or more fiducials positioned on or in thesubstrate for determining the alignment of the normalization device inan image of the normalization device such that the position in the imageof each of the one or more compartments in the first arrangement can bedetermined using the one or more fiducials.

Embodiment 276: The normalization device of any of Embodiments 271-275,wherein the substrate comprises a first layer, and at least some of theplurality of compartments are positioned in the first layer in a firstarrangement.

Embodiment 277: The normalization device of Embodiment 276, wherein thesubstrate further comprises a second layer positioned above the firstlayer, and at least some of the plurality of compartments are positionedin the second layer including in a second arrangement.

Embodiment 278: The normalization device of Embodiment 277, furthercomprising one or more additional layers positioned above the secondlayer, and at least some of the plurality of compartments are positionedwithin the one or more additional layers.

Embodiment 279: The normalization device of any one of Embodiments271-278, wherein at least one of the compartments is configured to beself-sealing such that the material can be injected into theself-sealing compartment and the compartment seals to contain theinjected material.

Embodiment 280: The normalization device of any of Embodiments 271-279,further comprising an adhesive on the proximal surface of the substrateand configured to adhere the normalization device to the body portionpatient.

Embodiment 281: The normalization device of any of Embodiments 271-280,further comprising a heat transfer material designed to transfer heatfrom the body portion of the patient to the material in the one or morecompartments.

Embodiment 282: The normalization device of any of Embodiments 271-280,further comprising an adhesive strip having a proximal side and a distalside, the proximal side configured to adhere to the body portion, theadhesive strip including a fastener configured to removably attach tothe proximal surface of the substrate.

Embodiment 283: The normalization device of Embodiment 282, wherein thefastener comprises a first part of a hook-and-loop fastener, and thefirst layer comprises a corresponding second part of the hook-and-loopfastener.

Embodiment 284: The normalization device of any of Embodiments 271-283,wherein substrate a flexible material to allow the substrate to conformto the shape of the body portion.

Embodiment 285: The normalization device of any of Embodiments 271-284,wherein the first arrangement includes a circular-shaped arrangements ofthe compartments.

Embodiment 286: The normalization device of any of Embodiments 271-284,wherein the first arrangement includes a rectangular-shaped arrangementsof the compartments.

Embodiment 287: The normalization device of any of Embodiments 271-286,wherein the material in at least two compartments is the same.

Embodiment 288: The normalization device of any of Embodiments 271-287,wherein at least one of a length, a width or a depth dimension of acompartment is less than 0.5 mm.

Embodiment 289: The normalization device of any of Embodiments 271-287,wherein a width dimension the compartments is between 0.1 mm and 1 mm.

Embodiment 290: The normalization device of Embodiment 289, wherein alength dimension the compartments is between 0.1 mm and 1 mm.

Embodiment 291: The normalization device of Embodiment 290, wherein adepth dimension the compartments is between 0.1 mm and 1 mm.

Embodiment 292: The normalization device of any of Embodiments 271-287,wherein at least one of the length, width or depth dimension of acompartment is greater than 1.0 mm.

Embodiment 293: The normalization device of any of Embodiments 271-287,wherein dimensions of some or all of the compartments in thenormalization device are different from each other allowing a singlenormalization device to have a plurality of compartments havingdifferent dimensions such that the normalization device can be used invarious medical image scanning devices having different resolutioncapabilities.

Embodiment 294: The normalization device of any of Embodiments 271-287,wherein the normalization device includes a plurality of compartmentswith differing dimensions such that the normalization device can be usedto determine the actual resolution capability of the scanning device.

Embodiment 295: A normalization device, comprising: a first layer havinga width, length, and depth dimension, the first layer having a proximalsurface and a distal surface, the proximal surface adapted to be placedadjacent to a surface of a body portion of a patient, the first layerincluding one or more compartments positioned in the first layer in afirst arrangement, each of the one or more compartments containing aknown material; and one or more fiducials for determining the alignmentof the normalization device in an image of the normalization device suchthat the position in the image of each of the one or more compartmentsin the first arrangement be the determined using the one or morefiducials.

Embodiment 296: The normalization device of Embodiment 295, furthercomprising a second layer having a width, length, and depth dimension,the second layer having a proximal surface and a distal surface, theproximal surface adjacent to the distal surface of the first layer, thesecond layer including one or more compartments positioned in the secondlayer in a second arrangement, each of the one or more compartments ofthe second layer containing a known material.

Embodiment 297: The normalization device of Embodiment 296, furthercomprising one or more additional layers each having a width, length,and depth dimension, the one or more additional layers having a proximalsurface and a distal surface, the proximal surface facing the secondlayer and each of the one or more layers positioned such that the secondlayer is between the first layer and the one or more additional layers,each of the one or more additional layers respectively including one ormore compartments positioned in each respective one or more additionallayers layer in a second arrangement, each of the one or morecompartments of the one or more additional layers containing a knownmaterial.

Embodiment 298: The normalization device of any one of Embodiments295-297, wherein at least one of the compartments is configured to beself-sealing such that the material can be injected into theself-sealing compartment and the compartment seals to contain theinjected material.

Embodiment 299: The normalization device of Embodiment 295, furthercomprising an adhesive on the proximal surface of the first layer.

Embodiment 300: The normalization device of Embodiment 295, furthercomprising a heat transfer material designed to transfer heat from thebody portion of the patient to the material in the one or morecompartments.

Embodiment 301: The normalization device of Embodiment 295, furthercomprising an adhesive strip having a proximal side and a distal side,the proximal side configured to adhere to the body portion, the adhesivestrip including a fastener configured to removably attach to theproximal surface of the first layer.

Embodiment 302: The normalization device of Embodiment 301, wherein thefastener comprises a first part of a hook-and-loop fastener, and thefirst layer comprises a corresponding second part of the hook-and-loopfastener.

Embodiment 303: The normalization device of Embodiment 295, wherein thenormalization device comprises a flexible material to allow thenormalization device to conform to the shape of the body portion.

Embodiment 304: The normalization device of Embodiment 295, wherein thefirst arrangement includes a circular-shaped arrangements of thecompartments.

Embodiment 305: The normalization device of Embodiment 295, wherein thefirst arrangement includes a rectangular-shaped arrangements of thecompartments.

Embodiment 306: The normalization device of Embodiment 295, wherein thematerial in at least two compartments of the first layer is the same.

Embodiment 307: The normalization device of any of Embodiments 296 or297, wherein the material in at least two compartments of any of thelayers is the same.

Embodiment 308: The normalization device of Embodiment 295, wherein atleast one of the one or more compartments include a contrast material.

Embodiment 309: The normalization device of Embodiment 308, wherein thecontrast material comprises one of iodine, Gad, Tantalum, Tungsten,Gold, Bismuth, or Ytterbium.

Embodiment 310: The normalization device of Embodiment 295, wherein atleast one of the one or more compartments include a materialrepresentative of a studied variable.

Embodiment 311: The normalization device of Embodiment 309, wherein thestudied variable is representative of calcium 1000 HU, calcium 220 HU,calcium 150 HU, calcium 130 HU, or a low attenuation (e.g., 30 HU)material.

Embodiment 312: The normalization device of Embodiment 295, wherein atleast one of the one or more compartments include a phantom.

Embodiment 313: The normalization device of Embodiment 312, wherein thephantom comprises one of water, fat, calcium, uric acid, air, iron, orblood.

Embodiment 314: The normalization device of Embodiment 295, wherein thefirst arrangement includes at least one compartment that contains acontrast agent, at least one compartment that includes a studiedvariable and at least one compartment that includes a phantom.

Embodiment 315: The normalization device of Embodiment 295, wherein thefirst arrangement includes at least one compartment that contains acontrast agent and at least one compartment that includes a studiedvariable.

Embodiment 316: The normalization device of Embodiment 295, wherein thefirst arrangement includes at least one compartment that contains acontrast agent and at least one compartment that includes a phantom.

Embodiment 317: The normalization device of Embodiment 295, wherein thefirst arrangement includes at least one compartment that contains astudied variable and at least one compartment that includes a phantom.

Embodiment 318: The normalization device of Embodiment 271, wherein thefirst arrangement of the first layer includes at least one compartmentthat contains a contrast agent, at least one compartment that includes astudied variable and at least one compartment that includes a phantom,and the second arrangement of the second layer includes at least onecompartment that contains a contrast agent, at least one compartmentthat includes a studied variable and at least one compartment thatincludes a phantom.

Embodiment 319: The normalization device of Embodiment 295, wherein atleast one of the length, width or depth dimension of a compartment isless than 0.5 mm.

Embodiment 320: The normalization device of Embodiment 295, wherein thewidth dimension the compartments is between 0.1 mm and 1 mm.

Embodiment 321: The normalization device of Embodiment 295, wherein thelength dimension the compartments is between 0.1 mm and 1 mm.

Embodiment 322: The normalization device of Embodiment 295, wherein thedepth (or height) dimension the compartments is between 0.1 mm and 1 mm.

Embodiment 323: The normalization device of Embodiment 295, wherein atleast one of the length, width or depth dimension of a compartment isgreater than 1.0 mm.

Embodiment 324: The normalization device of any one of Embodiments295-297, wherein the dimensions of some or all of the compartments inthe normalization device are different from each other allowing a singlenormalization device to have a plurality of compartments havingdifferent dimension such that the normalization device can be used invarious medical image scanning devices having different resolutioncapabilities.

Embodiment 325: The normalization device of any one of Embodiments295-297, wherein the normalization device includes a plurality ofcompartments with differing dimensions such that the normalizationdevice can be used to determine the actual resolution capability of thescanning device.

Embodiment 326: A computer-implemented method for normalizing medicalimages for an algorithm-based medical imaging analysis, whereinnormalization of the medical images improves accuracy of thealgorithm-based medical imaging analysis, the method comprising:accessing, by a computer system, a first medical image of a region of asubject and the normalization device, wherein the first medical image isobtained non-invasively, and wherein the normalization device comprisesa substrate comprising a plurality of compartments, each of theplurality of compartments holding a sample of a known material;accessing, by the computer system, a second medical image of a region ofa subject and the normalization device, wherein the second medical imageis obtained non-invasively, and wherein the first medical image and thesecond medical image comprise at least one of the following: one or morefirst variable acquisition parameters associated with capture of thefirst medical image differ from a corresponding one or more secondvariable acquisition parameters associated with capture of the secondmedical image, a first image capture technology used to capture thefirst medical image differs from a second image capture technology usedto capture the second medical image, and a first contrast agent usedduring the capture of the first medical image differs from a secondcontrast agent used during the capture of the second medical image;identifying, by the computer system, image parameters of thenormalization device within the first medical image; generating anormalized first medical image for the algorithm-based medical imaginganalysis based in part on the first identified image parameters of thenormalization device within the first medical image; identifying, by thecomputer system, image parameters of the normalization device within thesecond medical image; and generating a normalized second medical imagefor the algorithm-based medical imaging analysis based in part on thesecond identified image parameters of the normalization device withinthe second medical image, wherein the computer system comprises acomputer processor and an electronic storage medium.

Embodiment 327: The computer-implemented method of Embodiment 326,wherein the algorithm-based medical imaging analysis comprises anartificial intelligence or machine learning imaging analysis algorithm,and wherein the artificial intelligence or machine learning imaginganalysis algorithm was trained using images that included thenormalization device.

Embodiment 328: The computer-implemented method of any of Embodiments326-327, wherein the first medical image and the second medical imageeach comprise a CT image and the one or more first variable acquisitionparameters and the one or more second variable acquisition parameterscomprise one or more of a kilovoltage (kV), kilovoltage peak (kVp), amilliamperage (mA), or a method of gating.

Embodiment 329: The computer-implemented method of Embodiment 328,wherein the method of gating comprises one of prospective axialtriggering, retrospective ECG helical gating, and fast pitch helical.

Embodiment 330: The computer-implemented method of any of Embodiments326-329, wherein the first image capture technology and the second imagecapture technology each comprise one of a dual source scanner, a singlesource scanner, Dual source vs. single source scanners dual energy,monochromatic energy, spectral CT, photon counting, and differentdetector materials.

Embodiment 331: The computer-implemented method of any of Embodiments326-330, wherein the first contrast agent and the second contrast agenteach comprise one of an iodine contrast of varying concentration or anon-iodine contrast agent.

Embodiment 332: The computer-implemented method of any of Embodiments326-327, wherein the first image capture technology and the second imagecapture technology each comprise one of CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 333: The computer-implemented method of any of Embodiments326-332, wherein a first medical imager that captures the first medicalimager is different than a second medical image that capture the secondmedical image.

Embodiment 334: The computer-implemented method of any of Embodiments326-333, wherein the subject of the first medical image is differentthan the subject of the first medical image.

Embodiment 335: The computer-implemented method of any of Embodiments326-333, wherein the subject of the first medical image is the same asthe subject of the second medical image.

Embodiment 336: The computer-implemented method of any of Embodiments326-333, wherein the subject of the first medical image is differentthan the subject of the second medical image.

Embodiment 337: The computer-implemented method of any of Embodiments326-336, wherein the capture of the first medical image is separatedfrom the capture of the second medical image by at least one day.

Embodiment 338: The computer-implemented method of any of Embodiments326-337, wherein the capture of the first medical image is separatedfrom the capture of the second medical image by at least one day.

Embodiment 339: The computer-implemented method of any of Embodiments326-338, wherein a location of the capture of the first medical image isgeographically separated from a location of the capture of the secondmedical image.

Embodiment 340: The computer-implemented method of any of Embodiments326-339, wherein the normalization device comprises the normalizationdevice of any of Embodiments 271-325.

Embodiment 340: The computer-implemented method of any of Embodiments326-339, wherein the normalization device comprises the normalizationdevice of any of Embodiments 271-325.

Embodiment 341: The computer-implemented method of any of Embodiments326-340, wherein the region of the subject comprises a coronary regionof the subject.

Embodiment 342: The computer-implemented method of any of Embodiments326-341, wherein the region of the subject comprises one or morecoronary arteries of the subject.

Embodiment 343: The computer-implemented method of any of Embodiments326-340, wherein the region of the subject comprises one or more ofcarotid arteries, renal arteries, abdominal aorta, cerebral arteries,lower extremities, or upper extremities of the subject.

Other Embodiment(s)

Although this invention has been disclosed in the context of certainembodiments and examples, it will be understood by those skilled in theart that the invention extends beyond the specifically disclosedembodiments to other alternative embodiments and/or uses of theinvention and obvious modifications and equivalents thereof. Inaddition, while several variations of the embodiments of the inventionhave been shown and described in detail, other modifications, which arewithin the scope of this invention, will be readily apparent to those ofskill in the art based upon this disclosure. It is also contemplatedthat various combinations or sub-combinations of the specific featuresand aspects of the embodiments may be made and still fall within thescope of the invention. It should be understood that various featuresand aspects of the disclosed embodiments can be combined with, orsubstituted for, one another in order to form varying modes of theembodiments of the disclosed invention. Any methods disclosed hereinneed not be performed in the order recited. Thus, it is intended thatthe scope of the invention herein disclosed should not be limited by theparticular embodiments described above.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. Theheadings used herein are for the convenience of the reader only and arenot meant to limit the scope of the inventions or claims.

Further, while the methods and devices described herein may besusceptible to various modifications and alternative forms, specificexamples thereof have been shown in the drawings and are hereindescribed in detail. It should be understood, however, that theinvention is not to be limited to the particular forms or methodsdisclosed, but, to the contrary, the invention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the various implementations described and the appendedclaims. Further, the disclosure herein of any particular feature,aspect, method, property, characteristic, quality, attribute, element,or the like in connection with an implementation or embodiment can beused in all other implementations or embodiments set forth herein. Anymethods disclosed herein need not be performed in the order recited. Themethods disclosed herein may include certain actions taken by apractitioner; however, the methods can also include any third-partyinstruction of those actions, either expressly or by implication. Theranges disclosed herein also encompass any and all overlap, sub-ranges,and combinations thereof. Language such as “up to,” “at least,” “greaterthan,” “less than,” “between,” and the like includes the number recited.Numbers preceded by a term such as “about” or “approximately” includethe recited numbers and should be interpreted based on the circumstances(e.g., as accurate as reasonably possible under the circumstances, forexample ±5%, ±10%, ±15%, etc.). For example, “about 3.5 mm” includes“3.5 mm.” Phrases preceded by a term such as “substantially” include therecited phrase and should be interpreted based on the circumstances(e.g., as much as reasonably possible under the circumstances). Forexample, “substantially constant” includes “constant.” Unless statedotherwise, all measurements are at standard conditions includingtemperature and pressure.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover: A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y, and atleast one of Z to each be present.

What is claimed is:
 1. A system for characterizing a change in calciumlevel based on image analysis of one or more medical images of asubject, the system comprising: one or more computer readable storagedevices configured to store a plurality of computer executableinstructions; and one or more hardware computer processors incommunication with the one or more computer readable storage devices andconfigured to execute the plurality of computer executable instructionsin order to cause the system to: access a first calcium level of thesubject and a first weighted measure of a first set of plaque parametersfor the subject, the first calcium level and the first set of plaqueparameters obtained at a first point in time, wherein the first set ofplaque parameters comprises one or more of a ratio or function of volumeor surface area, heterogeneity index, geometry, radiodensity, orlocation of one or more regions of plaque of the subject at the firstpoint in time; access a second calcium level of the subject and one ormore medical images of the subject, the second calcium level and the oneor more medical images obtained at a second point in time, wherein theone or more medical images of the subject comprises the one or moreregions of plaque; determine a change in calcium level of the subjectbetween the first point in time and the second point in time; generate asecond weighted measure of a second set of plaque parameters for thesubject, the second set of plaque parameters derived from analyzing theone or more medical images, wherein the second set of plaque parameterscomprises one or more of a ratio or function of volume or surface area,heterogeneity index, geometry, radiodensity, or location of the one ormore regions of plaque at the second point in time; analyze a changebetween the first weighted measure of the first set of plaque parametersand the second weighted measure of the second set of plaque parameters;and characterize the change in calcium level of the subject between thefirst point in time and the second point in time based at least in parton the analyzed change between the first weighted measure of the firstset of plaque parameters and the second weighted measure of the secondset of plaque parameters.
 2. The system of claim 1, wherein the systemis further caused to generate a proposed treatment for the subject basedat least in part on the characterized change in calcium level of thesubject between the first point in time and the second point in time. 3.The system of claim 1, wherein a decrease in a ratio of volume tosurface area of the one or more regions of plaque between the firstpoint in time and the second point in time is indicative of a positivecharacterization of the change in calcium level of the subject betweenthe first point in time and the second point in time.
 4. The system ofclaim 1, wherein a decrease in the heterogeneity index of the one ormore regions of plaque between the first point in time and the secondpoint in time is indicative of a positive characterization of the changein calcium level of the subject between the first point in time and thesecond point in time.
 5. The system of claim 1, wherein theheterogeneity index of the one or more regions of plaque is determinedby generating a spatial mapping of radiodensity values within the one ormore regions of plaque.
 6. The system of claim 1, wherein theheterogeneity index of the one or more regions of plaque is determinedby generating a three-dimensional histogram of radiodensity valueswithin a geometric shape of the one or more regions of plaque.
 7. Thesystem of claim 1, wherein the heterogeneity index of the one or moreregions of plaque is determined by generating a mathematical transformof radiodensity values within a geometric shape of the one or moreregions of plaque.
 8. The system of claim 1, wherein the system isfurther caused to determine a change in a ratio or function of volumeand radiodensity of the one or more regions of plaque between the firstpoint in time and the second point in time, wherein the change incalcium level of the subject is further characterized based at least inpart the determined change in the ratio or function of volume andradiodensity of one or more regions between the first point in time andthe second point in time.
 9. The system of claim 1, wherein the changein calcium level of the subject is characterized for one or more of avessel, segment, or region of plaque of the subject.
 10. The system ofclaim 1, wherein the change in calcium level of the subject ischaracterized for a vessel segment of the subject.
 11. The system ofclaim 1, wherein the one or more regions of plaque are within a coronaryregion of the subject.
 12. The system of claim 1, wherein the first setof plaque parameters and the second set of plaque parameters furthercomprise a diffuseness of the one or more regions of plaque.
 13. Thesystem of claim 1, wherein the system is further caused to: analyze theone or more medical images of the subject to identify one or more newregions of plaque; and characterize the change in calcium level of thesubject between the first point in time and the second point in timefurther based at least in part on the analysis to identify one or morenew regions of plaque.
 14. The system of claim 1, wherein the secondcalcium level of the subject is determined by analyzing the one or moremedical images of the subject.
 15. The system of claim 1, wherein theone or more medical images of the subject comprises an image obtainedfrom a non-contrast Computed Tomography (CT) scan.
 16. The system ofclaim 1, wherein the one or more medical images of the subject comprisesan image obtained from a contrast-enhanced CT scan.
 17. The system ofclaim 1, wherein the change in calcium level between the first point intime and the second point in time is characterized as one of positive,neutral, or negative.
 18. The system of claim 17, wherein positivecharacterization of the change in calcium score is indicative of plaquestabilization.
 19. The system of claim 1, wherein the change in calciumlevel of the subject is characterized using machine learning.
 20. Thesystem of claim 1, wherein the first weighted measure is generated byweighting the accessed first set of plaque parameters logarithmically oralgebraically.
 21. A computer-implemented method of characterizing achange in calcium level based on image analysis of one or more medicalimages of a subject, the method comprising: accessing, by a computersystem, a first calcium level of the subject and a first weightedmeasure of a first set of plaque parameters for the subject, the firstcalcium level and the first set of plaque parameters obtained at a firstpoint in time, wherein the first set of plaque parameters comprises oneor more of a ratio or function of volume or surface area, heterogeneityindex, geometry, radiodensity, or location of one or more regions ofplaque of the subject at the first point in time; accessing, by thecomputer system, a second calcium level of the subject and one or moremedical images of the subject, the second calcium level and the one ormore medical images obtained at a second point in time, wherein the oneor more medical images of the subject comprises the one or more regionsof plaque; determining, by the computer system, a change in calciumlevel of the subject between the first point in time and the secondpoint in time; generating, by the computer system, a second weightedmeasure of a second set of plaque parameters for the subject, the secondset of plaque parameters derived from analyzing the one or more medicalimages, wherein the second set of plaque parameters comprises one ormore of a ratio or function of volume or surface area, heterogeneityindex, geometry, radiodensity, or location of the one or more regions ofplaque at the second point in time; analyzing, by the computer system, achange between the first weighted measure and the second weightedmeasure; and characterizing, by the computer system, the change incalcium level of the subject between the first point in time and thesecond point in time based at least in part on the analyzed changebetween the first weighted measure and the second weighted measure,wherein the computer system comprises a computer processor and anelectronic storage medium.
 22. The method of claim 21, furthercomprising generating, by the computer system, a proposed treatment forthe subject based at least in part on the characterized change incalcium level of the subject between the first point in time and thesecond point in time.
 23. The method of claim 21, wherein a decrease ina ratio of volume to surface area of the one or more regions of plaquebetween the first point in time and the second point in time isindicative of a positive characterization of the change in calcium levelof the subject between the first point in time and the second point intime.
 24. The method of claim 21, wherein a decrease in theheterogeneity index of the one or more regions of plaque between thefirst point in time and the second point in time is indicative of apositive characterization of the change in calcium level of the subjectbetween the first point in time and the second point in time.
 25. Themethod of claim 21, wherein the heterogeneity index of the one or moreregions of plaque is determined by generating a spatial mapping ofradiodensity values within the one or more regions of plaque.
 26. Themethod of claim 21, wherein the heterogeneity index of the one or moreregions of plaque is determined by generating a three-dimensionalhistogram of radiodensity values within a geometric shape of the one ormore regions of plaque.
 27. The method of claim 21, further comprisingdetermining, by the computer system, a change in a ratio or function ofvolume and radiodensity of the one or more regions of plaque between thefirst point in time and the second point in time, wherein the change incalcium level of the subject is further characterized based at least inpart the determined change in the ratio or function of volume andradiodensity of one or more regions between the first point in time andthe second point in time.
 28. The method of claim 21, wherein the changein calcium level of the subject is characterized using machine learning.29. The method of claim 21, wherein the change in calcium level betweenthe first point in time and the second point in time is characterized asone of positive, neutral, or negative.
 30. The method of claim 29,wherein positive characterization of the change in calcium score isindicative of plaque stabilization.